Method and system for planning, performing, and assessing high-throughput screening of multicomponent chemical compositions and solid forms of compounds

• US 20030059837A1
• Filed: 05/10/2002
• Published: 03/27/2003
• Est. Priority Date: 01/07/2000
• Status: Abandoned Application

• Current Assignee: Christopher B. Moore, Christopher McNulty, Douglas A. Levinson
• Original Assignee: Christopher B. Moore, Christopher McNulty, Douglas A. Levinson
• Inventors: Levinson, Douglas A., McNulty, Christopher, Moore, Christopher B.
• Application Number:
  US10/142,812
• Publication Number:
  US 20030059837A1
• Time in Patent Office: Days
• US Class Current: 435/7.1
• CPC Class Codes: B01J 2219/007 Simulation or vitual synthesis
• CPC Class Codes: B01J 2219/00756 Compositions, e.g. coatings, crystals, formulations
• CPC Class Codes: G01N 30/8617 Filtering, e.g. Fourier filtering
• CPC Class Codes: G01N 30/8693 Models, e.g. prediction of retention times, method development and validation
• CPC Class Codes: G16B 35/00 ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
• CPC Class Codes: G16C 20/30 Prediction of properties of chemical compounds, compositions or mixtures
• CPC Class Codes: G16C 20/60 In silico combinatorial chemistry
• CPC Class Codes: G16C 20/64 Screening of libraries
• CPC Class Codes: G16C 20/70 Machine learning, data mining or chemometrics

Abstract

A method and system for planning and assessing the results of high-throughput solid form screening and high-throughput formulation screening are disclosed. Also disclosed are methods and systems for using high-throughput solid form screening and high-throughput formulation screening to select compounds and formulations for further testing, or to prioritize testing.

Claims

1. A method for determining a formulation of a pharmaceutical, comprising the steps of:

performing high-throughput formulation screening of the pharmaceutical;

computing an optimization algorithm to select a plurality of molecular descriptors and a model accepting the molecular descriptors as parameters to optimize the predictive power of the model;

determining the formulation of the pharmaceutical.

3. The method of claim 1, further comprising the steps of:

generating values of experimental parameters using the model;

performing high-throughput screening using the generated values.

comparing the high-throughput experimental results with the results predicted by the model;

adjusting the model based on the high-throughput experimental results.

5. The method of claim 3 or 4, wherein the generated values are targeted to find an extremum of an expected property of an experiment.

6. The method of claim 3 or 4, wherein the generated values are targeted to determine boundaries between solid forms.

7. The method of claim 3 or 4, wherein the generated values are targeted to determine regions in which desired properties of formulations change rapidly with respect to changes experimental parameters.

8. The method of claim 3 or 4, wherein the generated values are targeted to determine regions in which desired properties of formulations change slowly with respect to changes experimental parameters.

9. The method of claim 3 or 4, wherein the generated values are targeted to a region of ambiguity or low confidence in classification or regression results.

10. The method of claim 1, 2, 3 or 4, wherein the predictive power is determined with respect to an extremum of an expected property of an experiment.

12. The method of claim 1, 2, 3 or 4, wherein the predictive power is determined with respect to regions in which desired properties of formulations or solid forms change rapidly with respect to changes in experimental parameters.

13. The method of claim 1, 2, 3 or 4, wherein the predictive power is determined with respect to one or more regions within class boundaries.

14. The method of claim 1, 2, 3 or 4, wherein the optimization algorithm comprises a stepwise algorithm.

15. The method of claim 1, 2, 3 or 4, wherein the optimization algorithm comprises a genetic algorithm.

16. The method of claim 1, 2, 3 or 4, wherein the optimization algorithm comprises simulated annealing.

17. The method of claim 1, 2, 3 or 4, wherein the model is a regression model.

18. The method of claim 1, 2, 3 or 4, wherein the model is a classifier.

19. The method of claim 1, 2, 3 or 4, wherein the model comprises linear regression.

20. The method of claim 1, 2, 3 or 4, wherein the model comprises stepwise linear regression.

21. The method of claim 1, 2, 3 or 4, wherein the model comprises an additive model.

22. The method of claim 1, 2, 3 or 4, wherein the model comprises projection pursuit regression.

23. The method of claim 1, 2, 3 or 4, wherein the model comprises recursive partitioning regression.

24. The method of claim 1, 2, 3 or 4, wherein the model comprises alternating conditional expectations.

25. The method of claim 1, 2, 3 or 4, wherein the model comprises additivity and variance stabilization.

26. The method of claim 1, 2, 3 or 4, wherein the model comprises locally weighted regression.

27. The method of claim 1, 2, 3 or 4, wherein the model comprises a neural network.

28. The method of claim 1, 2, 3 or 4, wherein the model comprises multivariate adaptive regression splines.

29. The method of claim 1, 2, 3 or 4, wherein the model comprises principal components regression.

30. The method of claim 1, 2, 3 or 4, wherein the model comprises partial least squares regression.

31. The method of claim 1, 2, 3 or 4, wherein the model comprises support vector regression.

32. The method of claim 1, 2, 3 or 4, wherein the model comprises a decision tree.

33. The method of claim 32, wherein the decision tree is generated an algorithm selected from the set consisting of C4.5, C5.0 or CART.

34. The method of claim 1, 2, 3 or 4, wherein the model comprises a support vector machine.

35. The method of claim 1, 2, 3 or 4, wherein the model comprises a k-nearest neighbor classifier.

36. The method of claim 1, 2, 3 or 4, wherein the model comprises a bayesian classifier.

37. The method of claim 36, wherein the model further comprises a probability density function determined using a Gaussian Mixture Model.

38. The method of claim 36, wherein the model further comprises a probability density function determined using Parzen windowing.

39. The method of claim 1, 2, 3 or 4, wherein the model comprises a self-organizing map.

40. The method of claim 1, 2, 3 or 4, wherein an approximately maximally diverse set of values of experimental parameters for high-throughput screening is generated using a diversification algorithm and a metric for measuring diversification.

41. The method of claim 1, 2, 3 or 4, wherein a set of values of experimental parameters for high-throughput screening is generated based on a structure-activity model.

70. The method of claim 1, 2, 3, or 4 further comprising the steps of:

based on the results of the high-throughput screening, generating a classifier to assign each solid form to a class.

71. The method of claim 70, wherein at least one class corresponds to a crystal polymorph.

72. The method of claim 70, wherein at least one class corresponds to a crystal habit.

73. The method of claim 70, wherein at least one class corresponds to a salt.

74. The method of claim 70, wherein at least one class corresponds to a hydrate.

75. The method of claim 70, wherein at least one class corresponds to a solvate.

76. The method of claim 70, wherein at least one class corresponds to a defined particle size range.

2. A method for generating a plurality of solid forms of a pharmaceutical, comprising the steps of:

performing high-throughput solid-form screening of the pharmaceutical;

computing an optimization algorithm to select a plurality of molecular descriptors and a model accepting the molecular descriptors as parameters to optimize the predictive power of the model;

determining the formulation of the pharmaceutical.

4. The method of claim 2, further comprising the steps of:

generating values of experimental parameters using the model;

performing high-throughput screening using the generated values.

comparing the high-throughput experimental results with the results predicted by the model;

adjusting the model based on the high-throughput experimental results.

11. The method of claim 2, wherein the predictive power is determined with respect to boundaries between solid forms.

42. A method for selecting a compound for further testing, comprising the steps of:

receiving information of a plurality of compounds;

performing high-throughput solid-form screening of at least one of the plurality of compounds to identify at least one solid-form;

based on the at least one property of each identified solid-form, selecting at least one of the plurality of compounds for further testing.

48. The method of claim 42, 43, 44, 45, 46, or 47, further comprising the steps of:

based on the results of the high-throughput screening, generating a model to estimate at least one property of the compound.

49. The method of claim 48, wherein the model is a regression model.

50. The method of claim 48, wherein the model is a classifier.

51. The method of claim 48, wherein the at least one property comprises solubility.

52. The method of claim 48, wherein the at least one property comprises bioavailability.

53. The method of claim 48, wherein the at least one property comprises dissolution.

54. The method of claim 53, wherein the at least one property further comprises dissolution time.

55. The method of claim 48, wherein the at least one property comprises stability.

56. The method of claim 48, wherein the at least one property comprises permeability.

57. The method of claim 48, wherein the at least one property comprises partitioning.

58. The method of claim 48, wherein the at least one property comprises a mechanical property.

59. The method of claim 58, wherein the mechanical property comprises compressibitility.

60. The method of claim 58, wherein the mechanical property comprises compactibility.

61. The method of claim 58, wherein the mechanical property comprises a flow characteristic.

62. The method of claim 58, wherein the mechanical property comprises compressibitility.

63. The method of claim 48, wherein the at least one property comprises color.

64. The method of claim 48, wherein the at least one property comprises taste.

65. The method of claim 48, wherein the at least one property comprises smell.

66. The method of claim 48, wherein the at least one property comprises absorption.

67. The method of claim 48, wherein the at least one property comprises toxicity.

68. The method of claim 48, wherein the at least one property comprises metabolic profile.

69. The method of claim 48, wherein the at least one property comprises potency.

77. The method of claim 48, wherein the model comprises linear regression.

78. The method of claim 48, wherein the model comprises stepwise linear regression.

79. The method of claim 48, wherein the model comprises an additive model.

80. The method of claim 48, wherein the model comprises projection pursuit regression.

81. The method of claim 48, wherein the model comprises recursive partitioning regression.

82. The method of claim 48, wherein the model comprises alternating conditional expectations.

83. The method of claim 48, wherein the model comprises additivity and variance stabilization.

84. The method of claim 48, wherein the model comprises locally weighted regression.

85. The method of claim 48, wherein the model comprises a neural network.

86. The method of claim 48, wherein the model comprises multivariate adaptive regression splines.

87. The method of claim 48, wherein the model comprises principal components regression.

88. The method of claim 48, wherein the model comprises partial least squares regression.

89. The method of claim 48, wherein the model comprises support vector regression.

90. The method of claim 48, wherein the model comprises a decision tree.

91. The method of claim 48, wherein the decision tree is generated an algorithm selected from the set consisting of C4.5, C5.0 or CART.

92. The method of claim 48, wherein the model comprises a support vector machine.

93. The method of claim 48, wherein the model comprises a k-nearest neighbor classifier.

94. The method of claim 48, wherein the model comprises a bayesian classifier.

95. The method of claim 94, wherein the model further comprises a probability density function determined using a Gaussian Mixture Model.

96. The method of claim 94, wherein the model further comprises a probability density function determined using Parzen windowing.

97. The method of claim 48, wherein the model comprises a self-organizing map.

98. The method of claim 42, 43, 44, 45, 46, or 47 further comprising the steps of:

applying at least one unsupervised learning or clustering algorithm to at least a subset of the results of the high-throughput screening.

99. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises hierarchical clustering.

100. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises agglomerative hierarchical clustering.

101. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises stepwise-optimal hierarchical clustering.

102. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises k-means clustering.

103. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises gausssian mixture model clustering.

104. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises self-organizing map-based clustering.

105. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises clustering using the Chameleon, DBSCan, CURE or ROCK algorithms.

106. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises unsupervised Bayesian learning.

107. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises principal component analysis.

108. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises nonlinear component analysis.

109. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises independent component analysis.

110. The method of claim 98 wherein the unsupervised learning or clustering algorithm comprises multidimensional scaling.

43. A method for selecting a compound for further testing, comprising the steps of:

receiving information of a plurality of compounds;

performing high-throughput formulation screening on at least one of the plurality of compounds;

based on at least one tested property, selecting at least one of the plurality of compounds for further testing.

44. A method for selecting a solid form of a compound for further testing, comprising the steps of:

receiving information of a compound;

performing high-throughput solid-form screening to identify at least two solid forms of the compound;

based on the results of the high-throughput solid-form screening, selecting a solid form of the compound for further testing.

45. A method for selecting a formulation of a compound for further testing, comprising the steps of:

receiving information of a compound;

performing high-throughput formulation screening of the compound;

based on the results of the high-throughput formulation screening, selecting a formulation of the compound for further testing.

46. A method for determining whether to further test at least one compound, comprising the steps of:

receiving information of the at least one compound;

performing high-throughput formulation screening of the at least one compound;

based on at least one tested property, determining whether to further test the at least one compound.

47. A method for determining whether to further test at least one compound, comprising the steps of:

receiving information of the at least one compound;

performing high-throughput solid-form screening of the at least one compound;

based on at least one tested property, determining whether to further test the at least one compound.

111. A method for selecting a compound for priority testing, comprising the steps of:

receiving information of a plurality of compounds;

performing high-throughput solid-form screening of at least one of the plurality of compounds to identify at least one solid-form;

based on the at least one property of each identified solid-form, selecting at least one of the plurality of compounds for further testing.

112. A method for selecting a compound for priority testing, comprising the steps of:

receiving information of a plurality of compounds;

performing high-throughput formulation screening on at least one of the plurality of compounds;

based on at least one tested property, selecting at least one of the plurality of compounds for further testing.

113. A method for selecting a solid form of a compound for priority testing, comprising the steps of:

receiving information of a compound;

performing high-throughput solid-form screening to identify at least two solid forms of the compound;

based on the results of the high-throughput solid-form screening, selecting a solid form of the compound for further testing.

114. A method for selecting a formulation of a compound for priority testing, comprising the steps of:

receiving information of a compound;

performing high-throughput formulation screening of the compound;

based on the results of the high-throughput formulation screening, selecting a formulation of the compound for further testing.

115. A method for determining whether to priority test at least one compound, comprising the steps of:

receiving information of the at least one compound;

performing high-throughput formulation screening of the at least one compound;

based on at least one tested property, determining whether to further test the at least one compound.

116. A method for determining whether to priority test at least one compound, comprising the steps of:

receiving information of the at least one compound;

performing high-throughput solid-form screening of the at least one compound;

based on at least one tested property, determining whether to further test the at least one compound.

117. A method for selecting a solid form of a compound for further testing, comprising the steps of:

receiving information of a compound;

performing high-throughput formulation screening to identify at least two solid forms of the compound;

based on the results of the high-throughput formulation screening, selecting a solid form of the compound for further testing.