Drug model explorer

• US 20050079511A1
• Filed: 02/06/2004
• Published: 04/14/2005
• Est. Priority Date: 10/14/2003
• Status: Abandoned Application

• Current Assignee: Tripos, L.P.
• Original Assignee: Pharsight Corporation (Certara Strategic Consulting)
• Inventors: Schwartz, Michael J., Vally, Jean-Max, Sheiner, Timothy Matthew, Mandema, Jacob W.
• Application Number:
  US10/773,767
• Publication Number:
  US 20050079511A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Computer systems and methods facilitate exploring results of drug candidate modeling. In one embodiment, the software is configured to receive raw data simulated by a probabilistic model of clinical safety, tolerability, and efficacy of a drug candidate. Index information is extracted from the raw data and then referenced to generate a metadata file, the structure of the metadata file explicitly reflecting a hierarchical structure of the model. The metadata file is in turn used to convert the raw data into a binary file, the metadata file explicitly identifying locations within the binary file, of treatment scenario information types and output performance information types. The metadata file is also referenced to generate an interface configured to receive inputs from a non-expert audience, and in turn present relevant subsets of the binary file in a limited number of plot and tabular formats. By standardizing presentation and manipulation of data from different models, software and methods in accordance with the present invention facilitate meaningful interaction between a non-expert audience, and the complex abstract mathematical models predicting drug behavior. The heightened audience-model interaction afforded by the present invention in turn promotes uniform and consistent evaluation of modeled data in the process of drug development.

Claims

1. A method of representing performance of a drug candidate, the method comprising:

receiving raw data generated by a model of drug candidate behavior, the raw data comprising index information, treatment scenario input information types, and corresponding output performance information types;

extracting the index information from the raw data;

referencing the extracted index information to generate a metadata file, a structure of the metadata file explicitly reflecting a hierarchical structure of the model;

referencing the metadata file to convert the raw data file into a binary file, the metadata file explicitly identifying locations of treatment scenario information types and the output performance information types within the binary file;

generating a user interface from the metadata file, the interface comprising a menu of input variables;

presenting the menu to a user;

receiving a user-selected input at the interface;

causing the interface to reference the metadata file and the binary file to identify a subset of the binary file relevant to the user-selected input; and

presenting the data subset in one of a select type of presentation formats at the interface.

2. The method of claim 1 wherein the data subset represents a clinical effect.

3. The method of claim 1 wherein the data subset represents a likelihood of a clinical effect lying within a range of user-defined value.

4. The method of claim 1 wherein the data subset represents a value of an independent variable required for a clinical effect to one of attain, exceed, and equal a user-defined value.

5. The method of claim 1 wherein the data subset represents a value of an independent variable required for a clinical effect to fall one of within, above, and below a user-defined range of values.

6. The method of claim 1 wherein the presentation format comprises a table.

7. The method of claim 1 wherein the presentation format comprises a matrix of tables.

8. The method of claim 1 wherein the presentation format comprises a plot.

9. The method of claim 1 wherein the presentation format comprises a matrix of plots.

10. The method of claim 1 wherein the data subset represents a contrast between output corresponding to two controllable variable input scenarios.

11. The method of claim 10 wherein the data subset represents a contrast between output corresponding to a first controllable variable input scenario featuring the drug candidate, and a second controllable variable input scenario featuring a competitor of the drug candidate.

12. The method of claim 10 wherein the contrast represents one of a difference, a ratio, and a log ratio.

13. The method of claim 1 wherein the menu of input variables is selected from the group consisting of an endpoint, a controllable variable, and an uncontrollable variable.

14. The method of claim 13 wherein endpoint is based upon a clinically measured value.

15. The method of claim 13 wherein the controllable variable is selected from the group comprising drug candidate identity, drug candidate dose, frequency of administration of drug candidate, and formulation of the drug candidate.

16. The method of claim 13 wherein the uncontrollable variable comprises a patient attribute selected from the group consisting of age, gender, body weight, and disease state.

17. The method of claim 13 wherein the uncontrollable variable comprises a model assumption.

18. The method of claim 1 wherein the raw data comprises a file organized according to explicit index values, and the metadata file encodes the explicit index values into a structure.

19. The method of claim 18 wherein the raw data comprises multiple files.

20. The method of claim 18 wherein the raw data is converted into the single binary file organized to match the encoded structure.

21. The method of claim 18 wherein the raw data is converted into multiple binary files organized to match the encoded structure.

22. The method of claim 18 wherein the explicit index values are encoded into an ordered tree structure.

23. The method of claim 22 wherein the binary file comprises an n-dimensional structure having a geometry matching the tree structure.

24. The method of claim 1 wherein the menu comprises text from the Metadata file.

25. The method of claim 1 further comprising drafting an additional conversion routine configured to recognize the raw data structure, and to transform the raw data into a standard metadata file format.

26. A computer system comprising a processor and a memory storing code to operate the processor, the code comprising, a parser module configured to receive raw data output by a model of drug candidate behavior, and to generate a metadata file encoding outputs and related inputs of the model based upon index information extracted from the raw data;

a data transfer module configured to convert the raw data into a binary file organized to match a structure encoded in the metadata file; and

a graphic user interface configured to present a menu of input variables to a user, to receive inputs selected by the user, to reference the metadata file and the binary file to identify a subset of the binary file relevant to the selected inputs, and to present the data subset in one of a select type of presentation format.

27. The computer system of claim 26 wherein the raw data comprises:

an index file having row vectors including a row number, the row vectors describing unique modeling input scenarios, and

a simulation output file comprising columns of number distributions produced by the model when run through a simulation process utilizing the specific input scenario, a column number corresponding to the row number; and

wherein,

the metadata file is organized according to a tree structure, and the binary file is organized into an n-dimensional structure whose geometry matches the tree structure.

28. The computer system of claim 26 wherein the parser module further comprises a conversion routine configured to recognize a format of the model, and to transform the raw data into a standard format of the metadata file.