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PASS APPLICATION IN R&D OF NEW PHARMACEUTICALS (adaptation of a text from Prof. Vladimir Poroikov, January 2005)

A new drug entity has to pass successfully the following steps*

  1. Ligand design (hits).

  2. Chemical synthesis and/or purchase of samples for biological testing.

  3. Ligand finding (leads): in vitro testing of the required specific biological activity.

  4. Ligand optimization (drug-candidates): in vivo confirmation of the required specific biological activity; investigation of general pharmacological/toxicological profile (no adverse/toxic effects at the appropriate doses) of the selected substances; investigation of pharmacokinetics of the selected substances (favorable absorption, distribution, metabolism and excretion characteristics).

  5. Submitting IND to get a permission of Drug Authority for clinical trials.

  6. Clinical trials, final proof of the concept.

  7. Submitting NDA to get an approval of Drug Authority for medical application of the drug-candidate.

At any stage, project may failure due to different reasons. More than 30% of failures in pharmaceutical R & D projects are due to the adverse/toxic effects, which are found at the later stages of the project when a lot of time and money are already spent (for nothing).

Typically, any chemical compound exhibits several or many kinds of biological activity, and the final goal of R&D is to select the compounds with the required pharmacological action but without unwanted adverse/toxic effects.

The whole complex of biological activities that might be revealed by chemical compound during its interaction with the human organism is called biological activity spectrum. It is not possible to test experimentally millions of available compounds against thousands known kinds of biological activity.

Computer program PASS predicts biological activity spectrum of compound based on its structural formula. In version of PASS 1.913 (December 2004) predicted biological activity spectrum includes 986 kinds of biological activity including:

-         677 actions on particular targets (e.g., 5 Hydroxytryptamine 2A antagonist, Acetylcholine M4 receptor agonist, Adenosine deaminase inhibitor, Alpha glucosidase inhibitor, Calcium channel N-type antagonist, Dipeptidyl peptidase IV inhibitor, Endothelin A receptor antagonist, Growth hormone release inhibitor, Insulin sensitizer, Leukotriene E4 antagonist, etc.);

-         44 actions on a particular infectious agent (Acaricide, Antifungal, Antihelmintic, Antimycobacterial, Anti-HIV, Anti-HCV, etc.);

-         226 pharmacotherapeutic effects (e.g., Analgesic, Antiarrhythmic, Bone formation stimulant, Bronchodilator, Cognition disorders treatment, Diuretic, Immunomodulator, Male reproductive disfunction treatment, Prostatic benign hyperplasia treatment, etc.);

-         44 adverse effects and toxicities (e.g., Arrhythmogenic, Cardiotoxic, Convulsant, Hypertensive, Mutagenic, Carcinogenic, Embryotoxic, etc.). 

Application of computer programs PASS (Prediction of Activity Spectra for Substances) and PharmaExpert provide the following opportunities: (1) to increase the number of hits in the sub-set of compounds selected for synthesis and biological testing, and (2) to filter out the hits with likely unwanted adverse/toxic action. 

Let us consider two examples of such PASS application: 

(1) Selection of compounds with anti-HIV activity.  

To analyze how PASS predictions can enrich the number of active compounds in the subset selected on the basis of computer prediction from the database of chemical compounds, we compared the results of anti-HIV activity prediction for the compounds from the Open NCI Database with the results of anti-HIV screening. Within the 250000 compounds from the Open NCI Database, the subset of 42689 compounds was tested versus anti-HIV activity, and the number of active compounds was found to be 1504. Thus, the percentage of actives in the tested subset of open NCI compounds is 3.52% (1504/42689). A random selection would therefore preserve this ratio. Using PASS prediction, even if the value Pa>10% is used as a threshold for selecting active compounds, the fraction of “actives” is enriched to a factor 2.2. At the highest threshold Pa>90%, the enrichment gets close to a factor of 17 [Poroikov et al., 2003].  

(2) Filtering compounds from the Prestwick Chemical Library. 

Prestwick Chemical Library [http://www.prestwickchemical.com] is a collection consisted of 880 carefully selected compounds, which are highly diverse in structure and cover many therapeutic areas – from neuropsychiatry to cardiology, immunology, anti-inflammatory, and more.  

Over 85% of these compounds are marketed drugs, for which both main pharmacological actions and some adverse/toxic effects are known. In particular, the convulsant effect is found for 49 compounds from this collection (e.g., acetazolamide, amitryptiline, arecoline, chlotpromazine, baclofen, buflomedil, bupivacaine, bupropion, clozapine, enoxacin, ethamivan, fenfluramine, haloperidol, hydrastine, iohexol, laudanosine, lidocaine, maprotiline, mefenamic acid, methazolamide, mianserine, mefloquine, metrizamide, naloxone, nefopam, orphenadrine, pimozide, propafenone, quinidine, quinacrine, terfenadine, theophylline, etc.).  

We obtained PASS predictions for all 880 compounds, and select 99 compounds, which are likely 5 hydroxytryptamine release stimulants and, therefore, might be applied as antidepressants. Analyzing the subset, we show that 74 of these compounds are also predicted as being convulsants with probability more than 40%.  

Using computer program PharmaExpert we retrieve the results of prediction for the Prestwick Chemical Library based on the following query: “5 hydroxytryptamine release stimulant with probability more than 50% NOT convulsant”. As a result, we obtain 12 compounds that correspond to this query. No one known convulsant was included into this sub-set.  

Some advantages of PASS use. 

Possibility of application at early stages of the research. Because only structural formula of compound (hit) is necessary as input for PASS, computer prediction can be obtained at the very early step of pharmaceutical R &D (ligand design) when no time & money are yet spent on chemical synthesis, biological testing, etc. 

Reasonable accuracy of prediction. Average accuracy of prediction in leave one out cross-validation (for ~57.000 compounds and ~1.000 kinds of biological activity from the PASS training set) is about 85%. PASS algorithm produce robust estimates of structure-activity relationships despite the incompleteness of the training set [Poroikov et al., 2000]. 

Predictions are rather fast. Calculation of biological activity spectra for 10.000 compounds on an ordinary PC takes about 5 min; therefore PASS can be effectively used to analyze the databases consisted of millions of structures. 

Standard structure format is used. Standard SDF-file format (http://www.mdli.com) is used as input for PASS; therefore, the existing databases of chemical structures can be easily retrieved. 

Possibility of creating the exclusive knowledgebase. The user can add new biologically active compounds and new kinds of biological activity to the training set, and create his knowledgebase(s); therefore, the “in house” proprietary data can be effectively applied for this purpose on the exclusive basis. 

Possibility of free testing. PASS prediction abilities can be freely tested via Internet [Sadym et al., 2003]. 

References for further reading 

Anzali S., Barnickel G., Cezanne B., Krug M., Filimonov D., Poroikov V. (2001). Discriminating between drugs and nondrugs by Prediction of Activity Spectra for Substances (PASS). J. Med. Chem., 44 (15), 2432-2437. 

Filimonov D., Poroikov V., Borodina Yu., Gloriozova T. (1999). Chemical Similarity Assessment through multilevel neighborhoods of atoms: definition and comparison with the other descriptors. J.Chem.Inf.Comput. Sci., 39 (4), p.666-670. 

Geronikaki A., Babaev E., Dearden J., Dehaen W., Filimonov D., Galaeva I., Krajneva V., Lagunin A., Macaev F., Molodavkin G., Poroikov V., Saloutin V., Stepanchikova A., Voronina T. (2004). Design of new anxiolytics: from computer prediction to synthesis and biological evaluation. Bioorg. Med. Chem., 12 (24), 6559-6568. 

Geronikaki A., Dearden J., Filimonov D., Galaeva I., Garibova T., Gloriozova T., Krajneva V., Lagunin A., Macaev F., Molodavkin G., Poroikov V., Pogrebnoi S., Shepeli F., Voronina T., Tsitlakidou M., Vlad L. (2004). Design of new cognition enhancers: from computer prediction to synthesis and biological evaluation. J. Med. Chem., 47 (11), 2870-2876. 

Lagunin A.A., Gomazkov O.A., Filimonov D.A., Gureeva T.A., Dilakyan E.A., Kugaevskaya E.V., Elisseeva Yu.E., Solovyeva N.I., Poroikov V.V. (2003). Computer-aided selection of potential antihypertensive compounds with dual mechanisms of action. J. Med. Chem., 46 (15), 3326-3332. 

Poroikov V.V., Filimonov D.A., Borodina Yu. V., Lagunin A.A., Kos A. (2000). Robustness of biological activity spectra predicting by computer program PASS for non-congeneric sets of chemical compounds. J. Chem. Inform. Comput. Sci., 40 (6), 1349-1355. 

Poroikov V., Akimov D., Shabelnikova E., Filimonov D. (2001). Top 200 medicines: can new actions be discovered through computer-aided prediction? SAR and QSAR in Environmental Research, 12 (4), 327-344. 

Poroikov V., Filimonov D. (2001). Computer-aided prediction of biological activity spectra. Application for finding and optimization of new leads. Rational Approaches to Drug Design, Eds. H.-D. Holtje, W.Sippl, Prous Science, Barcelona, p.403-407. 

Poroikov V.V., Filimonov D.A. (2002). How to acquire new biological activities in old compounds by computer prediction. J. Comput. Aid. Molec. Des., 16 (11), 819-824

Poroikov V., Lagunin A. (2002). PharmaExpert: knowledge-based computer system for interpretation of biological activity spectrum for substance. Newsletter of The QSAR and Modelling Society, No.13, p.23-24. 

Poroikov V.V., Filimonov D.A., Ihlenfeldt W.-D., Gloriozova T.A., Lagunin A.A., Borodina Yu.V., Stepanchikova A.V., Nicklaus M.C. (2003). PASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser. J. Chem. Inform. Comput. Sci., 43 (1) 228-236. 

Poroikov V., Filimonov D. (2005). PASS: Prediction of Biological Activity Spectra for Substances. In: Predictive Toxicology. Ed. by Christoph Helma. N.Y.: Marcel Dekker, 459-478. 

Sadym A., Lagunin A., Filimonov D., Poroikov V. (2003). Prediction of biological activity spectra via Internet. SAR and QSAR in Environmental Research, 14 (5-6), 339-347. 

Stepanchikova A.V., Lagunin A.A., Filimonov D.A., Poroikov V.V. (2003). Prediction of biological activity spectra for substances: Evaluation on the diverse set of drugs-like structures. Current Med. Chem., 10 (3), 225-233.


* To simplify the scheme, some technological stages (e.g., process development, dosage form development, etc.) are omitted in the steps given above.


Further applications with PASS


  • How to select promising candidates from supplier databases! Follow this hyperlink.

  • See http://cactus.nci.nih.gov/ncidb2.2/ for a WWW browser to the new and enlarged collection of open NCI database compounds (>250,000 structures) which includes the PASS parameters for all these compounds.



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