Predictive Chemoinformatics Applications to the Pharmaceutical Industry
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Informatics predictive chemoinformatics applications to the pharmaceutical industry While significant advances in chemoinformatics present tremendous opportunities to improve human health, the future of chemoinformatics in the pharmaceutical industry is not without significant challenges. hemoinformatics is the result of collective development, chemoinformatics provides the tools By Dr Leslie J. advances in chemistry, biology, computer to compare expression of genes and proteins as well Browne and C sciences and statistics and refers to the as complex signalling processes in disease and nor- Laurie L.Taylor electronic tools, methods and data used for analy- mal tissues and impacts concretely on selection of sis and predictive computation of drug effects on therapeutic targets (Figure 2). Differential gene and complex biological processes (Figure 1). Scientific protein expression profiles related to a disease state milestones over the last 50 years which have con- (eg cancer) promise to help fine-tune diagnoses and tributed to the evolution of predictive chemoinfor- improve the accuracy of prognostic indicators to matics include the development of the DNA double best serve individual patient needs. Chemists can helix model by Watson and Crick (1953); sequenc- now design compounds with improved drug-like ing of the first protein-bovine insulin by Sanger qualities through computerised structure/activity (1955); protein crystallography by Perutz (1954); the first integrated circuit by Kilby at Texas Instruments (1958); recombinant DNA technology Figure 1 by Berg et al (1972); conception of the Internet by Human genome Cerf and Kahn (1974); development of 2-D gel sequenced 3000Mbp D. melangaster genome electrophoresis (1975); identification of protein sequenced 2001 structure NMR by Wuthrich (1980); creation of S. cerevisiae E.Coli 2000 genomes sequenced the first personal computers by IBM (1981); poly- H. influenzae genome 1996 merase chain reaction technology by Mullis et al sequenced 1995 Expressed Sequence (1985); creation of the SWISS-PROT database Tags (Ventner) 1994 Milestones in predictive (1986); the founding of the NCBI (1988); creation WWW protocols 1991 chemoinformatics of BLAST by Altschul (1990); development of developed (CERN) BLAST search programme WWW protocols by the CERN (1991); identifica- 1990 NCBI created (Altschul et al) tion and significance of ESTs by Ventner (1991); founded 1988 SWISS-PROT database sequencing of the entire genomes of H. influenzae, 1986 established Polymerase S.cerevisiae (12Mb), E. Coli (1995-1996); D Personal Computers Chain Reaction 1985 developed (IBM) Recombinant First protein melangaster (180Mb) in 2000; and the human (Mullis et al) 1981 2D gel DNA sequence DNA Double Helix 1980 genome 3000Mbp in 2001. electrophoresis (Berg et al) (Sanger) (Watson Crick) Protein Structure 1975 1974 by NMR (Wuthrich) 1972 Advances in chemoinformatics related to Internet 1958 1955 1954 1953 genomics, proteomics and computer-assisted chem- (Cerf and Kahn) First integrated Protein circuit (Kilby at TI) Crystallography ical modelling hold tremendous promise to improve (Perutz) human health. For pharmaceutical research and Drug Discovery World Fall 2002 71 Informatics Genes and drug response DNA Nucleus Cell membrane DRUG TARGETS DNA bases Chain of amino acids Gene mRNA Ribosome Altered protein Efficacy DNA Protein Variable vs variants variants responses toxicity Figure 2 modelling – often reducing the number of com- so high – about 1 in 10 drug candidates survives Cellular checkpoints for pounds tested, compared with conventional trial- from initiation of clinical evaluation to market therapeutic intervention and-error methods. Drugs themselves affect expres- launch (Figure 4) – even a modest improvement to sion of a wide variety of genes and proteins, and 1 in 5 halves the development cost. Many drug fail- individual patient responses to drugs differ in ures are the result of ‘off target’ activity, ie poor metabolism and toxicity. side effect profiles that offset the potential thera- Pharmaceutical companies are highly motivated peutic effect. Structure-based design algorithms to reduce the discovery-to-market time and cost. and structure-activity data of existing bioactive Increased R&D dollars dedicated to the business of compounds facilitate the design of new compounds discovering new therapeutics have not resulted in a with the critical ‘drug-like’ qualities, in addition to correspondingly increased number of successful potency and efficacy at the therapeutic target: a drugs on the market. The pre-market failure rate of necessity for successful pre-clinical and clinical drug candidates has been measured and remea- development. The ability to project the in vitro sured from varying perspectives but always leads effects of a candidate drug into predictive models to the unavoidable conclusion that the process is of broader in vivo systemic effects earlier in the inefficient. More than 50% of failures are due to discovery process, will benefit the industry by lack of efficacy or unexpected animal toxicity. reducing failure rates, the developer by reducing (Figure 3). It now costs an average of $800 million costs and the consumer by helping get better drugs to bring a new product to market1. This includes, to the market. of course, the cost of the numerous failures and Despite the expense and time committed to their consumption of R&D dollars – a cost that is drug development, approved drugs have fre- passed on to the consumer. Since the failure rate is quently been withdrawn from the market due to 72 Drug Discovery World Fall 2002 Informatics severe adverse drug reactions (ADR). Between October 1997 and September 1998, a number of Causes of drug failure FDA-approved drugs were withdrawn, but not before being prescribed to 20 million patients in the US alone2. Animal toxicity Pharmacokinetics Importantly, the side effects that resulted in the 17% 7% ADR might have been measured and potentially designed out of the drug candidates had there been a means of identifying in advance the full spectrum Miscellaneous 7% of its potential side effects. While additional pre- market animal and human evaluation might decrease the number of drugs withdrawn from the market, the additional cost would be significant. In contrast, new chemoinformatics tools can be used Adverse effects to identify potential liabilities and benefits much 16% earlier in the discovery process. Identifying and Efficacy eliminating likely failures earlier permits efforts to Commercial 46% be focused on higher quality compounds, resulting 7% in more efficacious drugs produced at lower over- Figure 3 all cost. Chemogenomics applied to the discovery of new therapeutic agents Overview: While the physiological response of animals to drug treatment is the mainstay of efficacy and safety evaluation for drug develop- ment, the nature of conventional pre-clinical evaluation methods means that only a few important physiological parameters can be Better efficacy and toxicology assessed at a time. The new options provided by predictions will reduce attrition genomics and proteomics is to assess broadly the effect of a compound on the system as a whole 400 1 by looking at the transcriptome and the pro- Validated Lead Candidate IND/Phase 1 Phase II Phase III NDA 3rd year teome. As the tools are developed, it will be pos- idea compound on market sible to look not only at mRNA in high through- 200 put but also the resultant individual protein, its 50 conformation and its phosphorylation state, etc 0.1 to get the fullest possible picture of what is hap- pening at the molecular level in response to com- 12 pound treatment. Chemogenomics – or ‘pharmacology with 0.01 genomics tools’ – combines the strengths of tradi- tional pharmacology and the mechanistic 3 approach to drug discovery. Since an intact bio- survivingFraction of programmes 1 logical system is the focus of the evaluation, it is 0.001 contextually information-rich. The effects of a Basic Discovery Pre-clinical Clinical compound are examined in the context of other biological processes it affects in addition to the Discovery Pre-clinical Clinical ADME ADME To xicity target for which it was designed. For example, To xicity 12% ADME 20% 33% 34% To xicity this approach allows for compensatory and regu- 40% 38% latory mechanisms to influence the phenotypic outcome, as measured by the genomic response of Efficacy Efficacy Efficacy the system. Furthermore, since the analysis views 40% 33% 50% all, or at least a large proportion of, induced Figure 4 genomic changes within an organism, an Drug Discovery World Fall 2002 73 Informatics Figure 5 COMPOUNDS Gene expression class signatures DNA crosslinking signatures Statin signatures PPARa signatures SIGNATURES NSAID signatures Hepatotox signatures Sulindac Busulfan Cisplatin Ibuprofen Naproxen Clofibrate Lovastatin enofibrate Dicumarol Fluvastatin Diolofenac Bezafibrate Simvastatin F Gemfibrozil Carboplatin Atorvastatin Progesterone Indomethacin Clofibric Acid Beta Estradiol Norethindrone Ethinylestradiol Ethylene Glycol Ethylene Diethylstilbestrol Carbon Tetrachloride 1 Naphthyl Isothiocya 1 Naphthyl Bis 2 Ethylhexyl Phtha Bis 2 Ethylhexyl improved understanding of the breadth of com- information effectively to make key drug discovery pound action on target-related genes, as well as decisions. One approach is to characterise the unrelated genes, is possible. effects of existing, well-understood drugs in While the immediate promise of chemoge- chemogenomic