Approaches to Drug Discovery

Abstract

The last decade has seen many exciting new medicines being introduced to the market, such as novel oral anticoagulants, novel anti-diabetics, highly effective antiviral agents against hepatitis C, oral MS therapies, or targeted cancer therapies to name just a few. For the first time, diseases with orphan drug designations, e.g., cystic fibrosis or rare blood disorders, are treated with new chemical or biological entities. Biologics have now reached center stage especially for the treatment of immune disorders and oncologic indications, with the anti-TNFα agent adalimumab being the best-selling drug in 2014. These impressive successes notwithstanding, the so-called patent cliff, a conceived lack of productivity in the pharmaceutical industry, increasing expenses to discover and develop new therapeutic agents and reimbursement challenges have put pressure on the community to only target highly innovative approaches and to focus resources in selected areas of high expertise. With increased investments over the last years pharma companies continue to support large R&D efforts, while new venture capital backed biotech companies have surfaced, and universities attempt to translate their basic research into products through collaborations and the build-up of screening centers. Apart from this dynamic, the underlying process of drug discovery has not changed dramatically. It still starts with a solid disease hypothesis linked to a target which then needs precise validation (D. Sim, K. Kauser, B. Nicke) before the highthroughput screening is started for lead identification. As pointed out by J. Eder and P.L. Herrling, phenotypic screens have gained more attention recently, where a cell-based assay is used to first identify leads and later – hopefully – the corresponding target. Intractable targets, discarded as non-druggable a decade ago, are tackled today (S. Knapp), and new chemical matter intercepting protein– protein interactions (C. Ottmann), a revived interest in natural products (E.F. van Herwerden, R. Su¨ssmuth), and powerful high-throughput synthesis (C. Rademacher, P.H. Seeberger) might help to dissect and address challenging pathways. Meanwhile classical medicinal chemistry can rely on improved predictive models (M.S. Lawless, M. Waldman, R. Fraczkiewicz, R.D. Clark) and strong in vitro assays (G. Langer) to identify and optimize leads. Understanding their pharmacokinetic properties (A. Reichel) is a prerequisite for lead refinement and candidate selection, before in vivo efficacy is demonstrated in relevant animal models (O.D. Slayden, H. Tru¨bel, B. Albrecht, J. Hoffmann) and the potential v candidates are subjected to a thorough safety assessments (C. Stark) for final triaging. Early identification of biomarkers to either select patients susceptible to a certain therapy or as surrogate marker for efficacy (T. Krahn) and computational models to simulate drug effects (J. Lippert) have become essential tools when entering the clinical phase.