Pharmacokinetic-based drug design tool and method

• US 6,647,358 B2
• Filed: 05/26/1999
• Issued: 11/11/2003
• Est. Priority Date: 09/14/1998
• Status: Expired due to Fees

• Current Assignee: Lion Bioscience AG (2invest AG)
• Original Assignee: Lion Bioscience AG (2invest AG)
• Inventors: Sinko, Patrick J., Norris, Daniel A., Grass, George M., Wehrli, John E., Leesman, Glen D.
• Primary Examiner(s): MORAN, MARJORIE A
• Application Number:
  US09/320,371
• Publication Number:
  US 20020013662A1
• Time in Patent Office: 1,630 Days
• Field of Search: 395/500.02, 709/19, 703/2, 703/11
• US Class Current: 703/2
• CPC Class Codes: A61P 31/12 Antivirals
• CPC Class Codes: G16B 35/00 ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry
• CPC Class Codes: G16C 20/62 Design of libraries
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention relates to a pharmacokinetic-based design and selection tool (PK tool) and methods for predicting absorption of an administered compound of interest. The methods utilize the tool, and optionally a separately operable component or subsystem thereof. The PK tool includes as computer-readable components: (1) input/output system; (2) physiologic-based simulation model of one or more segments of a mammalian system of interest having one or more physiological barriers to absorption that is based on the selected route of administration; and (3) simulation engine having a differential equation solver. The invention also provides methods for optimizing as well as enabling minimal input requirements a physiologic-based simulation model for predicting in vivo absorption, and optionally one or more additional properties, from either in vitro or in vivo data. The PK tool of the invention may be provided as a computer system, as an article of manufacture in the form of a computer-readable medium, or a computer program product and the like. Subsystems and individual components of the PK tool also can be utilized and adapted in a variety of disparate applications for predicting the fate of an administered compound. The PK tool and methods of the invention can be used to screen and design compound libraries, select and design drugs, as well as predict drug efficacy in mammals from in vitro and/or in vivo data of one or more compounds of interest. The PK tool and methods of the invention also finds use in selecting, designing, and preparing drug compounds, and multi-compound drugs and drug formulations (i.e., drug delivery system) for preparation of medicaments for use in treating mammalian disorders.

Claims

1. A method of optimizing a model for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the method comprising:

(i) assigning an initial value to a selected adjustment parameter of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value for the selected adjustment parameter in the model with the new value selected in step (iv).

2. The method of claim 1, wherein the first and second data are stored in the same data source.

3. The method of claim 1, wherein the first data is stored in a first data source and the second data is stored in a second data source.

4. The method of claim 1, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters with the new value selected in step (iv).

5. The method of claim 1, wherein the selectively optimized adjustment parameter values are selected from the group consisting of constants, ranges of constants, functions and algorithms.

6. The method of claim 1, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

7. The method of claim 6, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

8. The method of claim 6, further comprising:

(vii) storing the selectively optimized adjustment parameter value in a database.

9. The method of claim 8, wherein the selectively optimized adjustment parameter values are stored in a computer-implemented database.

10. The method of claim 1, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

11. The method of claim 10, wherein the physiologic pharmacokinetic model determines the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

12. The method of claim 10, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

13. The method of claim 10, wherein the mammalian system of interest is human.

14. The method of claim 10, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

15. The method of claim 14, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

16. The method of claim 14, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

17. The method of claim 10, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

18. The method of claim 17, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

19. The method of claim 18, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

20. The method of claim 19, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

21. The method of claim 20 wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in viva properties.

22. The method of claim 1, wherein the selecting step comprises a curve-fitting algorithm.

23. The method of claim 22, wherein the curve-fitting algorithm is a regression-based algorithm.

24. The method of claim 22, wherein the curve-fitting algorithm is a plurality of curve fitting algorithms.

25. The method of claim 24, wherein the plurality of curve fitting algorithms are used simultaneously.

26. A computer-implemented method of optimizing a model for predicting an in vivo property of a compound in a mammalian system of interest, the method comprising:

(i) providing a computer containing the model;

(ii) assigning an initial value to a selected adjustment parameter of the model;

(iii) inputting first data for a plurality of compounds into the model on the computer to generate output data;

(iv) comparing the output data with second data for the plurality of compounds;

(v) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iv) is reduced; and

(vi) replacing the value for the selected adjustment parameter in the model with the new value selected in step (v).

27. The method of claim 26, wherein the first and second data are stored in the same data source.

28. The method of claim 26, wherein the first data is stored in a first data source and the second data is stored in a second data source.

29. The method of claim 26, further comprising:

(vii) repeating steps (iii)-(vi) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

30. The method of claim 29, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

31. The method of claim 26, further comprising:

(vii) storing the selected adjustment parameter value in a computer-implemented database.

32. The method of claim 26, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (ii) assigns an initial value to each of the plurality of selected adjustment parameters, step (v) selects a new value for one or more of the plurality of selected adjustment parameters and step (vi) replaces the value for the one or more of the plurality of selected adjustment parameters with the new value selected in step (v).

33. The method of claim 26, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function and an algorithm.

34. The method of claim 26, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

35. The method of claim 26, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

36. The method of claim 26, wherein the mammalian system of interest is human.

37. The method of claim 26, wherein the physiologic pharmacokinetic model determines the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

38. The method of claim 37, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

39. The method of claim 38, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

40. The method of claim 38, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

41. The method of claim 37, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

42. The method of claim 41, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

43. The method of claim 41, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

44. The method of claim 43, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

45. The method of claim 44 wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in vivo properties.

46. The method of claim 26, wherein the selecting step comprises a curve-fitting algorithm.

47. The method of claim 46, wherein the curve-fitting algorithm is a plurality of curve fitting algorithms.

48. The method of claim 47, wherein the provided computer contains the curve fitting algorithms and wherein the plurality of curve fitting algorithms are used simultaneously.

49. The method of claim 46, wherein the curve-fitting algorithm is a regression-based algorithm.

50. The method of claim 49, wherein the provided computer contains the curve fitting algorithm.

51. A computer-implemented method of generating optimized values for adjustment parameters of a physiologic-based model, the model predicting an in vivo property of a compound in a mammalian system of interest from an in vitro property of the compound, the method comprising:

(i) providing a computer having a curve-fitting algorithm and the physiologic-based model of the mammalian system of interest, the model comprising two or more equations, the model having as an output a predicted in vivo property of a compound in the mammalian system as a function of time, and at least two of the two or more equations are each modified by at least one adjustment parameter;

(ii) assigning an initial value to each of a plurality of adjustment parameters of the model;

(iii) inputting data from a first data source into the model and running the model to generate output data, the first data source containing first data for a plurality of compounds;

(iv) comparing the output data with a second data source, the second data source containing second data for the plurality of compounds;

(v) selecting a new value for one or more of the plurality of adjustment parameters such that deviation of the comparison in step (iv) is reduced using at least the curve-fitting algorithm on the computer; and

(vi) replacing the value for the one or more of the plurality of adjustment parameters in the model with the new value selected in step (v).

52. The method of claim 51, further comprising:

(vii) repeating steps (iii)-(vi) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

53. The method of claim 52, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

54. The method of claim 51, further comprising:

(vii) storing the adjustment parameter values in a database.

55. The method of claim 54, wherein the database is in a computer-implemented database.

56. The method of claim 51, wherein the first data is derived from testing each of the plurality of compounds for an in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

57. The method of claim 51, wherein the second data is derived from testing each of the plurality of compounds for an in vivo property in the mammalian system of interest.

58. The method of claim 51, wherein the first data corresponds to one or more in vitro properties and the second data corresponds to one or more in vivo properties, the in vivo properties selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

59. The method of claim 51, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

60. The method of claim 59, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

61. The method of claim 51, wherein the physiologic-based model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

62. The method of claim 61, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

63. The method of claim 61, wherein the mammalian system of interest is human.

64. The method of claim 61, wherein the physiologic pharmacokinetic model calculates the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more of the anatomical segments as a function of time.

65. The method of claim 64, wherein the second data corresponds to one or more in vivo properties for each compound of the plurality of compounds.

66. The method of claim 65, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

67. The method of claim 51, wherein the curve fitting algorithm fits a plurality of curves and the fitting is simultaneous.

68. The method of claim 51, wherein the curve-fitting algorithm is a regression-based algorithm.

69. A computer-implemented method of generating optimized values for a plurality of selected adjustment parameters of a physiologic-based model, the model predicting an in vivo property of a compound in a first mammalian system from an in vivo property of the compound in a second mammalian system, the method comprising:

(i) providing a computer having a curve-fitting algorithm and the physiologic-based model of the first mammalian system, the model having as an output an in vivo property of a compound in the first mammalian system as a function of time, and wherein the model comprises the plurality of selected adjustment parameters;

(ii) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(iii) inputting data from first data for a plurality of compounds into the model and running the model to generate output data, the first data containing in vivo data for each of a plurality of compounds in the second mammalian system;

(iv) comparing the output data with second data containing in vivo data for each of the plurality of compounds in the first mammalian system;

(v) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iv) is reduced using at least the curve-fitting algorithm on the computer; and

(vi) replacing the value of the one or more of the plurality of selected adjustment parameters in the model with the new value selected in step (v).

70. The method of claim 69, further comprising:

(vi) repeating steps (iii)-(vi) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

71. The method of claim 70, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

72. A computer implemented method of generating a selectively optimized adjustment parameter of a physiologic pharmacokinetic model of a mammalian system of interest that corresponds to a second species of mammal for predicting a pharmacokinetic property of a compound in the mammalian system from in vivo data obtained from a first species of mammal, the method comprising:

(i) providing a computer having a physiologic pharmacokinetic model of the mammalian system of interest, the model calculates as one or more output variables a pharmacokinetic property of a compound in the mammalian system as a function of time, wherein the model comprises a selected adjustment parameter;

(ii) assigning an initial value to the selected adjustment parameter of the model;

(iii) inputting data from a first data source into the model and running the model to generate output data, the first data source containing the first data for a plurality of compounds;

(iv) comparing the output data with a second data source, the second data source containing data for the plurality of compounds in the mammalian system of interest;

(v) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iv) is reduced using at least the curve-fitting algorithm on the computer; and

(vi) replacing the value of the selected adjustment parameter in the model with the new value selected in step (v).

73. The method of claim 72, wherein the plurality compounds comprise compounds having different pharmacokinetic properties in the mammalian system of interest.

74. The method of claim 72, wherein the model comprises equations having one or more input variables.

75. A computer-implemented method for optimizing a physiologic pharmacokinetic model for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the method comprising:

(i) providing a computer having a curve-fitting algorithm and the physiologic pharmacokinetic model of the mammalian system of interest, the model having as an output an in vivo property of a compound in the mammalian system as a function of time, and wherein the model comprises a plurality of selected adjustment parameters;

(ii) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(iii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iv) comparing the output data with second data for the plurality of compounds in the mammalian system of interest;

(v) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iv) is reduced using at least the curve-fitting algorithm on the computer; and

(vi) replacing the value for the one or more of the plurality of selected adjustment parameter values in the model with the new value selected in step (v).

76. The method of claim 75, further comprising:

(vii) repeating steps (iii)-(vi) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

77. A device for selectively optimizing a model for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the device comprising:

assigning means for assigning an initial value to a selected adjustment parameter;

inputting means for inputting first data for a plurality of compounds into the model;

running means for running the model to generate output data;

comparing means for comparing the output data with second data for the plurality of compounds;

selecting means for selecting a new value for the selected adjustment parameter such that deviation of the comparison from the comparison means is reduced; and

replacing means for replacing the value of the selected adjustment parameter in the model with the new value selected by the selecting means.

78. The device of claim 77, wherein the first and second data are stored in the same data source.

79. The device of claim 77, wherein the first data is stored in a first data source and the second data is stored in a second data source.

80. The device of claim 77, further comprising:

repeating means for using the inputting means, the running means, the comparing means, the selecting means, and the replacing means one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

81. The device of claim 80, wherein the repeating means determines the difference by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

82. The device of claim 77, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein the assigning means assigns an initial value to each of the plurality of selected adjustment parameters, the selecting means selects a new value for one or more of the plurality of selected adjustment parameters, and the replacing means replaces the value for the one or more of the plurality of selected adjustment parameters with the new value selected by the selecting means.

83. The device of claim 77, further comprising:

storing means for storing the value of the selected adjustment parameter in a database.

84. The device of claim 77, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function and an algorithm.

85. The device of claim 77, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

86. The device of claim 85, wherein the physiologic pharmacokinetic model comprises means for determining the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

87. The device of claim 85, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

88. The device of claim 85, wherein the mammalian system of interest is human.

89. The device of claim 85, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

90. The device of claim 89, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

91. The device of claim 89, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

92. The device of claim 85, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

93. The device of claim 92, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

94. The device of claim 92, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

95. The device of claim 94, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

96. The device of claim 95, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in vivo properties.

97. The device of claim 77, wherein the selecting means comprises a curve-fitting algorithm.

98. The device of claim 97, wherein the curve-fitting algorithm is a regression-based algorithm.

99. The device of claim 97, wherein the curve-fitting algorithm is a plurality of curve fitting algorithms.

100. The device of claim 99, wherein the plurality of curve-fitting algorithms are a plurality of simultaneous curve-fitting algorithms.

102. The method of claim 100, wherein the first and second data are stored in the same data source.

103. The method of claim 100, wherein the first data is stored in a first data source and the second data is stored in a second data source.

104. The method of claim 100, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

105. The method of claim 104, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

106. The method of claim 100, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters with the new value selected in step (iv).

107. The method of claim 100, further comprising:

storing the selected adjustment parameter value in a database.

108. The method of claim 100, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function and an algorithm.

109. The method of claim 100, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

110. The method of claim 109, wherein the physiologic pharmacokinetic model determines the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

111. The method of claim 109, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

112. The method of claim 109, wherein the mammalian system of interest is human.

113. The method of claim 109, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

114. The method of claim 113, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

115. The method of claim 113, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

116. The method of claim 109, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

117. The method of claim 116, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

118. The method of claim 116, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

119. The method of claim 118, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

120. The method of claim 119, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest.

101. A method for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the method comprising:

providing a model, wherein the model comprises a selected adjustment parameter and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the selected adjustment parameter in the model with the new value selected in step (iv); and

using the model to predict a pharmacokinetic property of a particular compound.

121. The method of claim 101, wherein the selecting step comprises a curve-fitting algorithm.

122. The method of claim 121, wherein the curve-filling algorithm is a regression-based algorithm.

123. The method of claim 121, wherein the curve-fitting algorithm is a plurality of curve-fitting algorithms.

124. The method of claim 123, wherein the plurality of curve-fitting algorithms are used simultaneously.

125. An article of manufacture comprising:

a computer readable medium; and

a data structure stored on the medium for predicting a pharmacokinetic property of a compound in a mammal of interest, wherein the data structure comprises a computer readable model of a selected mammalian system, wherein the computer readable model comprises a selected adjustment parameter, and wherein the data structure further comprises a value obtained by means for;

(i) assigning an initial value to the selected adjustment parameter of the model;

(ii) receiving first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in (iii) is reduced; and

(v) replacing the value of the selected adjustment parameter in the model with the new value selected in (iv).

126. The article of manufacture of claim 125, wherein the first and second data are stored in the same data source.

127. The article of manufacture of claim 125, wherein the first data is stored in a first data source and the second data is stored in a second data source.

128. The article of manufacture of claim 125, further comprising:

(vi) means for repeating (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

129. The article of manufacture of claim 128, wherein the repeating means determines the difference by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

130. The article of manufacture of claim 125, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein (i) assigns an initial value to each of the plurality of selected adjustment parameters, (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in (iv).

131. The method of claim 125, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function, an algorithm, a plurality of algorithms, and a plurality of functions.

132. The article of manufacture of claim 125, wherein the computer readable model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

133. The article of manufacture of claim 132, wherein the physiologic pharmacokinetic model comprises means for determining the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

134. The article of manufacture of claim 125, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

135. The article of manufacture of claim 125, wherein the mammalian system of interest is human.

136. The article of manufacture of claim 125, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

137. The article of manufacture of claim 136, wherein the first data is derived from testing each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

138. The article of manufacture of claim 136, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

139. The article of manufacture of claim 135, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

140. The article of manufacture of claim 139, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

141. The article of manufacture of claim 139, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

142. The article of manufacture of claim 141, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

143. The article of manufacture of claim 142, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in vivo properties.

144. The article of manufacture of claim 125, wherein the selecting means comprises a curve-fitting algorithm.

145. The article of manufacture of claim 144, wherein the curve-fitting algorithm is a regression-based algorithm.

146. The article of manufacture of claim 144, wherein the curve-fitting algorithm is a plurality of curve-fitting algorithms.

147. The article of manufacture of claim 146, wherein the plurality of curve-fitting algorithms are a plurality of simultaneous curve-fitting algorithms.

148. A computer program product for predicting a pharmacokinetic property of a compound in a mammal, the product comprising:

a computer readable medium; and

a program stored on the medium for predicting a pharmacokinetic property of a compound in a mammal of interest, wherein the program comprises a computer readable model of a selected mammalian system, the computer readable model comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises values obtained by;

(i) assigning an initial value to the selected adjustment parameter of the model;

(ii) inputting data from first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with data from second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the selected adjustment parameter in the model with the new value selected in step (iv).

149. The product of claim 148, wherein the first and second data are stored in the same data source.

150. The product of claim 148, wherein the first data is stored in a first data source and the second data is stored second data source.

151. The product of claim 148, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

152. The product of claim 151, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

153. The product of claim 148, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

154. The product of claim 148, wherein the selected mammalian system has one or more physiological barriers to absorption based on a selected route of administration.

155. The product of claim 148, wherein the computer readable model models one or more of fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility, and mass absorption for one or more segments of the selected mammalian system.

156. An article of manufacture for predicting a pharmacokinetic property of a compound in a mammal comprising:

a computer readable medium; and

a program stored on the medium for predicting a pharmacokinetic property of a compound in a mammal, wherein the program comprises a computer readable model of a selected mammalian system, the computer readable model comprising a plurality of selectively optimized adjustment parameters, and wherein the selectively optimized adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced;

(v) replacing the value for one or more of the plurality of selected adjustment parameter values in the model with the new value selected in step (iv).

157. The article of manufacture of claim 156, wherein the first and second data are stored in the same data source.

158. The article of manufacture of claim 156, wherein the first data is stored in a first data source and the second data is stored in a second data source.

159. The article of manufacture of claim 156, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

160. The article of manufacture of claim 159, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

161. The article of manufacture of claim 156, wherein the mammalian system is human.

162. The article of manufacture of claim 156, wherein the mammalian system is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

163. The article of manufacture of claim 162, wherein the mammalian system is gastrointestinal tract.

164. The article of manufacture of claim 163, wherein the computer readable model models segments of the gastrointestinal tract and the segments are selected from the group consisting of stomach, duodenum, jejunum, ileum and colon.

165. The article of manufacture of claim 156, wherein the first data comprises in vitro data.

166. The article of manufacture of claim 165, wherein the in vitro data is obtained from testing of a compound in one or more assays that generate data selected from the group consisting of cell, tissue, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR) data.

167. The article of manufacture of claim 165, wherein the plurality of compounds comprise compounds having different pharmacokinetic properties in the mammalian system.

168. The article of manufacture of claim 156, wherein the first data comprises in vivo data from a first species of mammal and the mammalian system comprises a second species of mammal.

169. The article of manufacture of claim 167, wherein the plurality of compounds comprise compounds having different pharmacokinetic properties in the mammalian system.

170. The article of manufacture of claim 156, wherein the plurality of selected adjustment parameters are applied to one or more of fluid absorption, flux, permeability, transport mechanism, transfer rate, and segment surface area.

171. The article of manufacture of claim 156, wherein input data for the computer readable model comprises data selected from the group consisting of dissolution rate, transport mechanism and formulation release rate.

172. The article of manufacture of claim 156, wherein the computer readable model has equations using one or more input variables corresponding to the input data for calculating as one or more output variables the change in the physiological property.

173. The article of manufacture of claim 156, wherein the pharmacokinetic property is selected from the group consisting of absorption, distribution, metabolism, elimination and toxicity.

174. The article of manufacture of claim 156, wherein the computer readable model uses data selected from the group consisting of concentration permeability, solubility, dissolution rate, transport mechanism, and formulation release rate as input data.

175. The article of manufacture of claim 156, wherein the computer readable model uses models selected from the group consisting of pH, fluid volume, fluid volume transfer rate, fluid absorption, surface area, and transit time.

176. The article of manufacture of claim 156, wherein the computer readable model includes models for one or more of absorption, distribution, metabolism, elimination and toxicity.

177. The article of manufacture of claim 156, wherein the computer readable model includes models for one or more of fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility, and mass absorption for one or more segments of the mammalian system.

178. The article of manufacture of claim 156, wherein the computer readable model comprises rules for one or more of transit, absorption, permeability, solubility, dissolution, and concentration for one or more segments of the mammalian system.

179. The article of manufacture of claim 156, wherein the computer model comprises a model of at least two anatomical segments and comprises a logic function module comprising a regional correlation estimation function and a control statement for initiating the function, wherein the estimation function when initiated generates an estimated value for a selected pharmacokinetic property of the compound in a first anatomical segment when supplied with an input value corresponding to the selected pharmacokinetic property in a second anatomical segment and with a regional correlation coefficient for the selected pharmacokinetic property of the first and second anatomical segments.

180. An article of manufacture comprising:

a computer readable medium; and

a program stored on the medium for predicting a pharmacokinetic property of a compound in a mammal of interest, wherein the program comprises a computer readable model of a selected mammalian system, the computer readable model comprises at least one selected adjustment parameter, and wherein at least the one selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the at least one selected adjustment parameter of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the at least one selected adjustment parameter such that deviation of the comparison in step (iii) is reduced;

(v) replacing the value for the at least one selected adjustment parameter in the model with the new value selected in step (iv).

181. The article of manufacture of claim 180, wherein the first and second data are stored in the same data source.

182. The article of manufacture of claim 180, wherein the first data is stored in a first data source and the second data is stored in a second data source.

183. The article of manufacture of claim 180, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

184. The article of manufacture of claim 183, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

185. The article of manufacture of claim 180, wherein the at least one selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

186. The article of manufacture of claim 180, wherein the computer readable model is a physiologic pharmacokinetic model of at least two of anatomical segments of the selected mammalian system and a logic function module comprising a regional correlation estimation function and a control statement for initiating the function wherein the estimation function when initiated generates an estimated value for a selected pharmacokinetic property of the compound in a first anatomical segment when supplied with an input value corresponding to the selected pharmacokinetic property in a second anatomical segment and with a regional correlation coefficient for the selected pharmacokinetic property of the first and second anatomical segments.

187. The article of manufacture of claim 186, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

188. The article of manufacture of claim 186, wherein the mammalian system of interest is human.

189. The article of manufacture of claim 186, wherein the pharmacokinetic property is selected from the group consisting of absorption, distribution, metabolism, elimination and toxicity.

190. The article of manufacture of claim 186, wherein the pharmacokinetic property is selected from the group consisting of permeability, solubility, dissolution rate and transport mechanism.

191. The article of manufacture of claim 186, wherein the input data comprises in vitro data.

192. The article of manufacture of claim 191, wherein the in vitro data is derived from testing of the compound in an assay that generates data selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) data.

193. The article of manufacture of claim 186, wherein the computer readable model uses equations selected from the group consisting fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility and mass absorption.

194. The article of manufacture of claim 193, wherein one or more of the equations is modified by a selectively optimized adjustment parameter.

195. The article of manufacture of claim 186, wherein the regional correlation estimation function comprises an algorithm.

196. The article of manufacture of claim 195, wherein the algorithm is selected from the group consisting of a polynomial, exponential, and logarithm.

197. An article of manufacture comprising:

a computer readable medium; and

a program stored on the medium for predicting absorption of a compound in a mammal of interest, wherein the program comprises a computer readable model of a selected mammalian system, wherein the computer readable model comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter of the model;

(ii) inputting data first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the selected adjustment parameter in the model with the new value selected in step (iv).

198. The article of manufacture of claim 197, wherein the first and second data are stored in the same data source.

199. The article of manufacture of claim 197, wherein the first data is stored in a first data source and the second data is stored in a second data source.

200. The article of manufacture of claim 197, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

201. The article of manufacture of claim 200, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

202. The article of manufacture of claim 197, wherein the mammalian system is selected from the group consisting of gastrointestinal tract, eye, nose, lung, skin and brain.

203. The article of manufacture of claim 202, wherein the mammalian system is the gastrointestinal tract.

204. The article of manufacture of claim 197, wherein the mammal is human.

205. The article of manufacture of claim 197, wherein the selected mammalian system has one or more physiological barriers to absorption of the compound based on a selected route of administration.

206. The article of manufacture of claim 198, wherein the computer readable model models one or more of fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility, and mass absorption for one or more segments of the mammalian system.

207. The article of manufacture of claim 197, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

208. The article of manufacture of claim 207, wherein the simulation model corresponds to a compartment-flow model comprising compartments that are operably linked through flow regulators modified by one or more converters.

209. The article of manufacture of claim 208, wherein the compartments comprise one or more compartments characterized by a parameter selected from the group consisting of fluid volume, fluid absorption, formulation, insoluble mass, soluble mass, and soluble mass absorption.

210. The article of manufacture of claim 208, wherein the flow regulators are characterized by a parameter selected from the group consisting of fluid absorption rate, fluid transit rate, formulation transit rate, formulation release rate, insoluble mass transit rate, insoluble mass dissolution rate, soluble mass transit rate, and soluble mass absorption rate.

211. The article of manufacture of claim 208, wherein the converters are characterized by a parameter selected from the group consisting of fluid volume, fluid volume absorption rate constant, fluid volume transit rate constant, insoluble mass, insoluble mass transit rate constant, dissolution rate constant, soluble mass, soluble mass transit rate constant, surface area, dissolved mass concentration and permeability.

212. The article of manufacture of claim 208, wherein one or more of the converters are characterized by one or more of the plurality of selected adjustment parameters.

213. The article of manufacture of claim 208, wherein one or more of the converters are characterized by a regional correlation parameter.

214. The article of manufacture of claim 197, wherein the model is a physiological model and the first data includes data selected from the group consisting of soluble mass transfer rate constant, permeability, solubility, dissolution rate, and transport mechanism.

215. The article of manufacture of claim 197, wherein the first data includes data selected from the group consisting of pH, initial fluid volume, surface area, fluid volume transit time, insoluble mass transit time, soluble mass transit time, fluid volume transfer rate, and fluid absorption rate.

216. The article of manufacture of claim 197, where the first and second data is selected from the group consisting of dissolution rate, transport mechanism, transit time, pH and formulation release rate.

217. The article of manufacture of claim 197, wherein the first data is in vitro data.

218. The article of manufacture of claim 217, wherein the in vitro data is permeability data derived from an assay selected from the group consisting of a cell-based assay and a tissue-based assay.

219. The article of manufacture of claim 217, wherein the in vitro data is transport mechanism data derived from an assay selected from the group consisting of a cell-based assay and a tissue-based assay.

220. The article of manufacture of claim 217, wherein the in vitro data is permeability data derived from structure-activity relationship data of the compound.

221. The article of manufacture of claim 217, wherein the in vitro data is dissolution rate data derived from structure-activity relationship data of the compound.

222. The article of manufacture of claim 217, wherein the in vitro data is solubility data derived from structure-activity relationship data of the compound.

223. A computer system for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the computer system comprising:

a computer; and

a program implementing a physiologic pharmacokinetic model on the computer, wherein the model comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for of the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value for the selected adjustment parameter in the model with the new value selected in step (iv).

224. The computer system of claim 223, wherein the first and second data are stored in the same data source.

225. The computer system of claim 223, wherein the first data is stored in a first data source and the second data is stored in a second data source.

226. The computer system of claim 223, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

227. The computer system of claim 226, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

228. The computer system of claim 223, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

229. The computer system of claim 223, wherein the selected adjustment parameter value is stored in a database on the computer.

230. The computer system of claim 223, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function, an algorithm, a plurality of functions, and a plurality of algorithms.

231. The computer system of claim 223, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

232. The computer system of claim 231, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

233. The computer system of claim 231, wherein the mammalian system of interest is human.

234. The computer system of claim 231, wherein the physiologic pharmacokinetic model determines the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

235. The computer system of claim 231, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

236. The computer system of claim 235, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

237. The computer system of claim 235, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

238. The computer system of claim 231, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

239. The computer system of claim 238, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

240. The computer system of claim 238, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

241. The computer system of claim 240, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

242. The computer system of claim 240, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in vivo properties.

243. The computer system of claim 223, wherein the selecting step comprises a curve-fitting algorithm.

244. The computer system of claim 243, wherein the curve-fitting algorithm is a regression-based algorithm.

245. The computer system of claim 243, wherein the curve-fitting algorithm is a plurality of curve-fitting algorithms.

246. The computer system of claim 245, wherein the plurality of curve-fitting algorithms are used simultaneously.

247. A method of predicting a pharmacodynamic or pharmacokinetic property of a compound in a mammal, the method comprising:

providing a model, wherein the model comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value for the selected adjustment parameter in the model with the new value selected in step (iv); and

using the model to predict the property of the compound.

248. The method of claim 247, wherein the first and second data are stored in the same data source.

249. The method of claim 247, wherein the first data is stored in a first data source and the second data is stored second data source.

250. The method claim 247, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

251. The method of claim 250, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

252. The method of claim 250, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

253. The method of claim 247, wherein the selected adjustment parameter value is selected from the group consisting of a constant, a range of constants, a function, an algorithm, a plurality of functions, and a plurality of algorithms.

254. The method of claim 247, further comprising:

storing the selected adjustment parameter value in a database.

255. The method of claim 247, wherein the model comprises a physiologic pharmacokinetic model of one or more anatomical segments of the mammal.

256. The method of claim 255, wherein the mammal is a mammalian system selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

257. The method of claim 255, wherein the mammal is human.

258. The method of claim 255, wherein the physiologic pharmacokinetic model determines the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

259. The method of claim 255, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

260. The method of claim 259, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

261. The method of claim 259, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination and toxicity.

262. The method of claim 255, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

263. The method of claim 262, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

264. The method of claim 255, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammal.

265. The method of claim 264, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

266. The method of claim 265, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammal for the one or more in vivo properties.

267. The method of claim 247, wherein the selecting step comprises a curve-fitting algorithm.

268. The method of claim 267, wherein the curve-fitting algorithm is a regression-based algorithm.

269. The method of claim 267, wherein the curve-fitting algorithm is a plurality of curve-fitting algorithms.

270. The method of claim 269, wherein the plurality of curve-fitting algorithms are used simultaneously.

271. A computer system for predicting absorption of a compound in a mammalian system of interest, the computer system comprising:

a computer; and

a program implementing an absorption model on the computer, wherein the model comprises at least one selected adjustment parameter and wherein the at least one selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the at least one selected adjustment parameter of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the at least one selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the at least one selected adjustment parameter value in the model with the new value selected in step (iv).

272. The computer system of claim 271, wherein the first and second data are stored in the same data source.

273. The computer system of claim 271, wherein the first data is stored in a first data source and the second data is stored in a second data source.

274. The computer system of claim 271, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

275. The computer system of claim 271, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

276. The computer system of claim 274, wherein the at least one selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

277. A computer system for predicting oral absorption of a compound in a mammal, the computer system comprising:

a computer; and

a program implementing an oral absorption model on the computer, wherein the model comprises a plurality of selected adjustment parameters, and wherein the plurality of selected adjustment parameters comprise values obtained by;

(i) assigning an initial value the selected adjustment parameter of the model;

(ii) inputting first data source containing first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the one or more of the plurality of selected adjustment parameters in the model with the new value selected in step (iv).

278. The computer system of claim 277, wherein the first and second data are stored in the same data source.

279. The computer system of claim 277, wherein the first data is stored in a first data source and the second data is stored in a second data source.

280. The computer system of claim 277, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

281. The computer system of claim 280, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

282. The computer system of claim 277, wherein at least one of the plurality of selected adjustment parameters is applied to one or more of fluid absorption, flux, permeability, transport mechanism, transfer rate, and segment surface area.

283. A computer system of claim 277, wherein the oral absorption model is a compartment-flow model.

284. The computer system of claim 277, wherein the oral absorption model comprises computer-implemented rules for one or more of transit, absorption, permeability, solubility, dissolution, and concentration for one or more segments of the GI tract of the mammal.

285. A computer system for predicting a property of a compound in a mammalian system, the computer system comprising:

a computer; and

a program on the computer, wherein the program comprises a plurality of selectively optimized adjustment parameters, the selectively optimized adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the program;

(ii) inputting first data for a plurality of compounds into the program and running the program to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the one or more of the plurality of selected adjustment parameters in the program with the new value selected in step (iv).

286. The computer system of claim 285, wherein the first and second data are stored in the same data source.

287. The computer system of claim 285, wherein the first data is stored in a first data source and the second data is stored in a second data source.

288. The computer system of claim 285, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

289. The computer system of claim 288, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

290. The computer system of claim 285, wherein the mammalian system is human.

291. The computer system of claim 285, wherein the mammalian system is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

292. The computer system of claim 291, wherein the mammalian system of interest is gastrointestinal tract, the gastrointestinal tract is modeled in segments and the segments are selected from the group consisting of stomach, duodenum, jejunum, ileum and colon.

293. The computer system of claim 285, wherein at least one of the plurality of selected adjustment parameters is applied to one or more of fluid absorption, flux, permeability, transport mechanism, transfer rate, and segment surface area.

294. The computer system of claim 285, wherein input data to the model includes data selected from the group consisting of dissolution rate, transport mechanism and formulation release rate.

295. The computer system of claim 285, wherein the first data is obtained from testing of a compound in one or more assays that generate data selected from the group consisting of cell, tissue, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR) data.

296. The computer system of claim 285, wherein the plurality of compounds comprise compounds having diverse pharmacokinetic properties in the mammalian system of interest.

297. The computer system of claim 285, wherein the first data comprises in vivo data from a first species of mammal and the mammalian system comprises a second species of mammal.

298. The computer system of claim 297, wherein the plurality of compounds comprise compounds having different pharmacokinetic properties in the mammalian system.

299. The computer system of claim 298, wherein the program comprises a model of at least two anatomical segments of the mammalian system and a logic function module comprising a regional correlation estimation function and a control statement for initiating the function, wherein the estimation function when initiated generates an estimated value for a selected pharmacokinetic property of the compound in a first anatomical segment when supplied with an input value corresponding to the selected pharmacokinetic property in a second anatomical segment and with a regional correlation coefficient for the selected pharmacokinetic property of the first and second anatomical segments.

300. The computer system of claim 285, wherein the program comprises equations for one or more of fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility, and mass absorption.

301. The computer system of claim 300, wherein the equations use initial parameter values corresponding to a physiological parameter and the plurality of selected adjustment parameters.

302. The computer system of claim 285, wherein the model uses equations and the equations comprise one or more input variables corresponding to the input data for calculating as one or more output variables the change in the property.

303. The computer system of claim 302, wherein the equations comprise one or more input variables corresponding to the input data for calculating as one or more output variables the change in an absorption property.

304. The computer system of claim 285, wherein the program comprises rules for one or more of transit, absorption, permeability, solubility, dissolution, and concentration for the mammalian system.

305. The computer system of claim 285, wherein the property is selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

306. The computer system of claim 285, wherein the property is selected from the group consisting of concentration, permeability, solubility, dissolution rate, transport mechanism, and formulation release rate.

307. The computer system of claim 285, wherein the property is selected from the group consisting of pH, fluid volume, fluid volume transfer rate, fluid absorption, surface area, and transit time.

308. The computer system of claim 285, further comprising a user interface on the computer.

309. A method of predicting a pharmacokinetic property of a compound in a mammalian system of interest, the method comprising:

providing a computer comprising an input system, an output system and a model, wherein the model comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced;

(v) replacing the value for the selected adjustment parameter with the new value selected in set (iv);

entering through the input system input data for the compound;

using the model to predict the pharmacokinetic property of the compound; and

outputting a predicted pharmacokinetic property of the compound with the output system.

310. The method of claim 309, wherein the first and second data are stored in the same data source.

311. The method of claim 309, wherein the first data is stored in a first data source and the second data is stored in a second data source.

312. The method of claim 309, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

313. The method of claim 312, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

314. The method of claim 309, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

315. The method of claim 309, wherein the computer further comprises a data processor and a memory and wherein the output system is at least a display.

316. The method of claim 315, wherein the computer is a standalone computer.

317. The method of claim 315, wherein the model is provided as computer readable program code.

318. The method of claim 317, wherein the computer readable program code is embodied in a computer readable medium.

319. The method of claim 317, wherein the computer readable program code is embodied in a memory.

320. The method of claim 315, wherein the input system and the output system comprise a user interface.

321. The method of claim 315, wherein the model models at least one of fluid transit, fluid absorption, mass transit, mass dissolution, mass solubility, and mass absorption.

322. The method of claim 315, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

323. The method of claim 315, wherein the first data comprises in vivo data from a first species of mammal and the mammalian system of interest corresponds to a second species of mammal.

324. The method of claim 323, wherein the plurality of compounds comprise compounds having different pharmacokinetic properties.

325. The method of claim 315, wherein the first data is obtained from testing of the compound in one or more assays that generate data selected from the group consisting of cell, tissue, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR) data.

326. The method of claim 315, wherein the plurality of compounds comprise compounds having different pharmacokinetic properties.

327. The method of claim 315, wherein the input data is selected from the group consisting of concentration, permeability, solubility, dissolution rate, transport mechanism, and formulation release rate.

328. The method of claim 315, wherein the input data further comprises data selected from the group consisting of dissolution rate, transport mechanism and formulation release rate.

329. The method of claim 315, wherein the pharmacokinetic property is selected from the group consisting of absorption, distribution, metabolism, elimination and toxicity.

330. The method of claim 315, wherein the pharmacokinetic property is selected from the group consisting of pH, initial fluid volume, surface area, transit time, fluid volume transfer rate, and fluid absorption.

331. The method of claim 315, wherein the mammalian system of interest is human.

332. The method of claim 315, wherein the model models one or more of transit, absorption, permeability, solubility, dissolution, and concentration for one or more segments of the mammal system of interest.

333. The method of claim 315, wherein the model includes equations for calculating as one or more output variables the change in the pharmacokinetic property.

334. The method of claim 315, wherein the model includes equations for one or more input variables corresponding to the input data for calculating as one or more output variables the change in an absorption parameter.

335. The method of claim 315, wherein the selected adjustment parameter modifies the portion of the model for one or more of fluid absorption, flux, permeability, transport mechanism, transfer rate, and segment surface area.

336. The method of claim 315, comprising:

reversibly storing in a computer-implemented database data corresponding to a predicted pharmacokinetic property of the compound.

337. The method of claim 315, wherein the model comprises a model of at least two anatomical segments and a logic function module comprising a regional correlation estimation function and a control statement for initiating the function wherein the estimation function when initiated is capable of generating an estimated value for a selected pharmacokinetic property of the compound in a first anatomical segment when supplied with an input value corresponding to the selected pharmacokinetic property in a second anatomical segment and with a regional correlation coefficient for the selected pharmacokinetic property of the first and second anatomical segments.

338. The method of claim 337, wherein the regional correlation estimation function comprises an algorithm.

339. The method of claim 338, wherein the algorithm is selected from the group consisting of a polynomial, exponential, and logarithm.

340. A computer system for predicting a property of a compound in a mammalian system of interest, the computer system comprising:

a computer; and

a program on the computer, wherein the program comprises a selected adjustment parameter, and wherein the selected adjustment parameter comprises a value obtained by;

(i) assigning an initial value to the selected adjustment parameter of the program;

(ii) inputting first data for a plurality of compounds into the program and running the program to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for the selected adjustment parameter such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the selected adjustment parameter in the program with the value selected in step (iv).

341. The computer system of claim 340, wherein the first and second data are stored in the same data source.

342. The computer system of claim 340, wherein the first data is stored in a first data source and the second data is stored in a second data source.

343. The computer system of claim 340, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

344. The method of claim 343, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

345. The method of claim 340, wherein the selected adjustment parameter is a plurality of selected adjustment parameters and wherein step (i) assigns an initial value to each of the plurality of selected adjustment parameters, step (iv) selects a new value for one or more of the plurality of selected adjustment parameters, and step (v) replaces the value for the one or more of the plurality of selected adjustment parameters and with the new value selected in step (iv).

346. The computer system of claim 340, wherein the program comprises a compartment-flow model for calculating time-dependent rate of absorption, extent of absorption, and concentration of a compound at a sampling site across a physiological barrier of one or more segments of the mammalian system.

347. The computer system of claim 346, wherein the selected adjustment parameter is selected from the group consisting of regional fluid absorption, permeability, flux, active transport, carrier mediated transport, compound efflux, transfer rate, and surface area.

348. The computer system of claim 346, wherein the property is selected from the group consisting of soluble mass transfer rate constant, permeability, solubility, dissolution rate, transport mechanism, pH, initial volume, surface area, transit time, fluid volume transfer rate, and fluid absorption rate.

349. A computer-implemented model of the gastrointestinal tract of a mammal for predicting a pharmacokinetic property of a compound in the mammal, the model comprising:

a computer; and

a program implementing a physiologic model on the computer, wherein the physiologic model comprises a plurality of selected adjustment parameters, and wherein the plurality of selected adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(ii) inputting data from first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with data from second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the one or more of the plurality of the selected adjustment parameters in the model with the new value selected in step (iv),

wherein the physiologic model comprises a compartment-flow model for calculating time-dependent rate of absorption, extent of absorption, and concentration of a compound at a sampling site across a physiological barrier of one or more segments of the mammalian system, wherein

the compartments are characterized by fluid volume, fluid absorption, insoluble mass, soluble mass, and mass absorption for one or more of segments of the GI tract of a mammal, the compartments are operably linked through flow regulators and converters, the flow regulators regulate flow among compartments and the converters modify the flow regulators, and wherein the flow regulators are characterized by fluid absorption rate, fluid transit rate, insoluble mass transit rate, insoluble mass dissolution rate, soluble mass transit rate, and soluble mass absorption rate.

350. The computer-implemented model of claim 349, wherein the first and second data are stored in the same data source.

351. The computer-implemented model of claim 349, wherein the first data is stored in a first data source and the second data is stored in a second data source.

352. The computer-implemented model of claim 349, further comprising:

(vi) repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

353. The computer-implemented model of claim 352, wherein the difference is determined by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

354. The computer-implemented model of claim 349, wherein the converters are characterized by fluid volume, fluid volume absorption rate constant, fluid volume transit rate constant, insoluble mass, insoluble mass transit rate constant, dissolution rate constant, soluble mass, soluble mass transit rate constant, surface area, dissolved mass concentration and permeability.

355. The computer-implemented model of claim 349, which further comprises compartments characterized by formulation and flow regulators characterized by formulation transit rate and formulation release rate.

356. The computer-implemented model of claim 349, further comprising:

compartments characterized by fluid volume and fluid volume absorption for stomach, duodenum, jejunum, ileum and colon that are operably linked through flow regulators and one or more converters that modify one or more of the flow regulators, wherein the flow regulators are characterized by fluid volume absorption rate and fluid volume transit rate, and wherein the converters are characterized by selected adjustment parameter values for one or more of fluid absorption rate constant and fluid volume transit rate constant.

357. A device for predicting a pharmacokinetic property of a compound in a mammalian system of interest, the device comprising:

modeling means for modeling at least one pharmacokinetic property of a compound in a mammal, wherein the modeling means comprises adjustment means for adjusting the model, and wherein the adjustment means is obtained by;

(i) assigning an initial value to the adjustment means;

(ii) inputting first data for a plurality of compounds into the modeling means and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds; and

(iv) selecting a new adjustment means such that deviation of the comparison means in step (iii) is reduced; and

applying means for applying the modeling means to predict a pharmacokinetic property of the compound.

358. The device of claim 357, wherein the first and second data are stored in the same data source.

359. The device of claim 357, wherein the first data is stored in a first data source and the second data is stored in a second data source.

360. The device of claim 357, further comprising:

(v) repeating means for repeating steps (ii)-(v) one or more times until a difference between the output data and the second data is less than the largest experimental error in the first or second data or less than the largest interday variation in the first or second data.

361. The device of claim 360, wherein the repeating means determines the difference by one of the following:

normalized difference, collective regression coefficient, normalized arithmetic mean, normalized median, normalized geometric mean, normalized harmonic mean, variance, standard deviation, or coefficient of variation.

362. The device of claim 357, wherein the adjustment means is a plurality of adjustment means and wherein step (i) assigns an initial value to each of the plurality of adjustment means and step (iv) selects a new value for one or more of the plurality of adjustment means.

363. The device of claim 357, wherein the adjustment means is selected from the group consisting of a constant, a range of constants, a function, an algorithm, a plurality of functions, and a plurality of algorithms.

364. The device of claim 357, wherein the modeling means is a physiologic pharmacokinetic model of one or more anatomical segments of the mammalian system of interest.

365. The device of claim 364, wherein the mammalian system of interest is selected from the group consisting of gastrointestinal tract, liver, heart, kidney, eye, nose, lung, skin and brain.

366. The device of claim 364, wherein the mammalian system of interest is human.

367. The device of claim 364, wherein the physiologic pharmacokinetic model comprises means for determining the change in one or more physiological parameters of the one or more anatomical segments and the movement and disposition of the compound in the one or more anatomical segments as a function of time.

368. The device of claim 364, wherein the first data corresponds to one or more in vitro properties for each of the plurality of compounds.

369. The device of claim 368, wherein the first data is derived from testing of each of the plurality of compounds for at least one in vitro property in an assay that generates data, the assay selected from the group consisting of cell, tissue, physicochemical, structure-activity relationship (SAR), and quantitative structure-activity relationship (QSAR).

370. The device of claim 368, wherein the in vitro properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

371. The device of claim 364, wherein the second data corresponds to one or more in vivo properties for each of the plurality of compounds.

372. The device of claim 371, wherein the in vivo properties are selected from the group consisting of absorption, distribution, metabolism, elimination, and toxicity.

373. The device of claim 371, wherein the plurality of compounds include compounds exhibiting different in vivo properties in the mammalian system of interest.

374. The device of claim 373, wherein the in vivo properties are selected from the group consisting of permeability, solubility, dissolution, activity, metabolism, and toxicity.

375. The device of claim 374, wherein the one or more in vivo properties are derived from testing each of the plurality of compounds in the mammalian system of interest for the one or more in vivo properties.

376. The device of claim 357, wherein the selecting step comprises a curve-fitting algorithm.

377. The device of claim 376, wherein the curve-fitting algorithm is a regression-based algorithm.

378. The device of claim 376, wherein the curve-fitting algorithm is a plurality of curve-fitting algorithms.

379. The device of claim 378, wherein the plurality of curve-fitting algorithms are a plurality of simultaneous curve-fitting algorithms.

380. A method of predicting a pharmacokinetic property of a compound in a mammalian system of interest, the method comprising:

providing a computer comprising an input system, an output system, a processor and an absorption model, wherein the model comprises a plurality of selected adjustment parameters, and wherein the selected adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value for the one or more at the plurality of adjustment parameters in the model with the new value selected in step (iv);

entering through the input system input data for the compound;

using the model to predict the absorption of the compound; and

outputting an absorption property of the compound with the output system.

381. A method for predicting solubility of a compound in the gastrointestinal (GI) tract of a mammal, the method comprising:

providing a solubility model, wherein the solubility model is a compartment-flow model, the model comprises a plurality of selected adjustment parameters, and wherein the selected adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value for the one or more of the plurality of the selected adjustment parameters in the model with the new value selected in step (iv), wherein

the compartments are characterized by insoluble mass and soluble mass for stomach, duodenum, jejunum, ileum and colon that are operably linked through flow regulators and one or more converters that modify one or more of the flow regulators, wherein the flow regulators are characterized by insoluble mass transit rate, insoluble mass dissolution rate, and soluble mass transit rate, wherein the converters are characterized by insoluble mass, insoluble mass transit rate constant, insoluble mass dissolution rate constant, soluble mass, and soluble mass transit rate constant, and wherein one or more of the converters are characterized by the selected adjustment parameters; and

using the solubility model to predict solubility of a particular compound.

382. A method for predicting absorption of a compound in the gastrointestinal (GI) tract of a mammal to at least the portal vein, the method comprising:

providing an absorption model, the absorption model corresponding to a compartment-flow model, wherein the model comprises a plurality of selected adjustment parameters, and wherein the plurality of selected adjustment parameters comprise values obtained by;

(i) assigning an initial value to each of the plurality of selected adjustment parameters of the model;

(ii) inputting first data for a plurality of compounds into the model and running the model to generate output data;

(iii) comparing the output data with second data for the plurality of compounds;

(iv) selecting a new value for one or more of the plurality of selected adjustment parameters such that deviation of the comparison in step (iii) is reduced; and

(v) replacing the value of the one or more of the plurality of the selected adjustment parameters in the model with the new value selected in step (iv), wherein

compartments in the model are characterized by soluble mass and soluble mass absorption for stomach, duodenum, jejunum, ileum and colon that are operably linked through flow regulators and one or more converters that modify the flow regulators, where the flow regulators are characterized by insoluble mass transit rate, insoluble mass dissolution rate, soluble mass transit rate, soluble mass absorption rate, where the converters are characterized by insoluble mass, insoluble mass dissolution rate constant, soluble mass transit rate constant, surface area, dissolved mass concentration, and permeability, and wherein one or more of the converters are modified by one or more of the plurality of selected adjustment parameters; and

using the absorption model to predict the absorption of a particular compound.