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Christopher Bailey (AstraZeneca) and Lee Goodwin (Covance) speak to Sankeetha Nadarajah (Managing Commissioning Editor, Bioanalysis) about the evolution of the Pharma-CRO working relationship between AstraZeneca and Covance.

Terpenoids constitute a significant fraction of molecules produced by living organisms that have found use in medicine and other industries. Problems associated with their procurement and adaptation for human use can be solved using chemical synthesis, which is an increasingly economical option in the modern era of chemistry. This article documents, by way of individual case studies, strategies for reducing the time and cost of terpene synthesis for drug discovery. A major trend evident in recent syntheses is that complex terpenes are increasingly realistic starting points for both medicinal chemistry campaigns and large-scale syntheses, at least in the context of the academic laboratory, and this trend will likely penetrate the commercial sector in the near future.

The productivity challenge facing the pharmaceutical industry is well documented. Strategies to improve productivity have mainly focused on enhancing efficiency, such as the application of Lean Six Sigma process improvement methods and the introduction of modeling and simulation in place of 'wet' experiments. While these strategies have their benefits, the real challenge is to improve effectiveness by reducing clinical failure rates. We advocate redesigning the screening cascade to identify and optimize novel compounds with improved efficacy against disease, not just with improved potency against the target. There should be greater use of disease-relevant phenotypic screens in conjunction with target-based assays to drive medicinal chemistry optimization. An opportunistic approach to polypharmacology is recommended. There should also be more emphasis on optimization of the molecular mechanism of action incorporating understanding of binding kinetics, consideration of covalent drug strategies and targeting allosteric modulators.

Hypertension, defined as elevated systolic blood pressure and/or diastolic blood pressure generally greater than 140/90 mmHg, is a significant risk factor for cardiovascular outcomes such as arterial aneurysm, myocardial infarction and stroke, and for nonvascular conditions such as Alzheimer's disease. The prevalence of the disease is rapidly increasing both in the USA and in the rest of the world. Hypertension can be managed to a degree through behavioral changes (e.g., reduction in salt intake and loss of excess body weight). When lifestyle changes fail, pharmacological therapy provides benefits, with combination drug therapy often required for many patients to reach their blood pressure-reduction goals. Approximately one-third of hypertensive patients who seek treatment fail to reach their goals, either because they are resistant to drug therapy or stop treatment due to side-effect issues. A medical need exists for new antihypertensive agents with improved risk-benefit profiles. However, within the past decade, the economics of bringing a new antihypertensive agent to market have become challenging due to the plethora of generic drugs available, the advent of polypharmacology, and the difficulty of identifying agents that are better than the standard of care. Only a few new mechanistic classes of antihypertensive agents have been recently approved, suggesting a lack of innovation within the industry. In this review, we describe the results of a survey of drug companies and drug classes in the 2007-2009 antihypertensive patent literature and comment on the current state of innovation in antihypertensive drug discovery.

The pharmaceutical industry is facing numerous, well-documented challenges - from the effects of patent expirations to high attrition rates in the drug-development pipeline. Future Medicinal Chemistry has invited a group of leaders from academia and industry to express their views on where the industry is heading and speculate as to what role medicinal chemists will play in the future.

The Drugs for Neglected Diseases initiative (DNDi) is a patients' needs-driven organization committed to the development of new treatments for neglected diseases. Created in 2003, DNDi has delivered four improved treatments for malaria, sleeping sickness and visceral leishmaniasis. A main DNDi challenge is to build a solid R&D portfolio for neglected diseases and to deliver preclinical candidates in a timely manner using an original model based on partnership. To address this challenge DNDi has remodeled its discovery activities from a project-based academic-bound network to a fully integrated process-oriented platform in close collaboration with pharmaceutical companies. This discovery platform relies on dedicated screening capacity and lead-optimization consortia supported by a pragmatic, structured and pharmaceutical-focused compound sourcing strategy.

High-content image analysis captures many cellular parameters, but current methods of interpretation of acquired multiple dimensions assume a normal distribution, which is rarely seen in biological data sets. We describe a novel statistically based approach that collapses a set of cellular measurements into a single value, permitting a simplified and unbiased comparison of heterogeneous cellular populations. Differences in multiple cellular responses across two populations are measured using nonparametric Kolmogorov-Smirnov (KS) statistics. This method can be used to study cellular functions, to identify novel target genes and pharmacodynamic biomarkers, and to characterize drug mechanisms of action.

For rapid anti-tuberculosis (TB) drug development in vitro pharmacokinetic/pharmacodynamic (PK/PD) models are useful in evaluating the direct interaction between the drug and the bacteria, thereby guiding the selection of candidate compounds and the optimization of their dosing regimens. Utilizing in vivo drug-clearance profiles from animal and/or human studies and simulating them in an in vitro PK/PD model allows the in-depth characterization of antibiotic activity of new and existing antibacterials by generating time–kill data. These data capture the dynamic interplay between mycobacterial growth and changing drug concentration as encountered during prolonged drug therapy. This review focuses on important PK/PD parameters relevant to anti-TB drug development, provides an overview of in vitro PK/PD models used to evaluate the efficacy of agents against mycobacteria and discusses the related mathematical modeling approaches of time–kill data. Overall, it provides an introduction to in vitro PK/PD models and their application as critical tools in evaluating anti-TB drugs.

This article provides a perspective on the growing significance of community and collaboration approaches in discovery and development (chemical and pharmaceutical). The drivers of and challenges to these developments are described and the knowledge management concepts that provide a foundation to scientific and cultural progress are explained. Principles and case study examples of community approaches to best practices and emerging approaches to knowledge-oriented collaboration are discussed. Collaboration is a complex activity involving many business factors, scientific issues and challenges, demanding the combination of a variety of knowledge management, cultural and organizational best practices. Flexible knowledge-oriented processes, support activities and services are required for effective collaboration results.

Background: The understanding of biomolecular interactions ultimately depends on knowledge about the structural and dynamic details of the interacting system. Rational structure-based drug design implements computational methodology in this rationale., Discussion: Together with increasing throughput of structural biology, molecular modeling has progressively contributed to rational drug design and elucidation of nontoxic and patient-tailored interventions, helping to make drug development more cost-efficient. But in this challenging time for the pharmaceutical industry, the successful discovery of novel therapeutics should rely on integration of computational modeling with experimentation when it comes to ligand-binding energetics, system flexibility and genetic diversity/heterogeneity of the target. Moreover, it appears that many drugs--even those for which specific receptors have been identified--intercalate in biological membranes, which could also become the actual target., Conclusions: Understanding the drug-target and drug-unwanted-target interactions at the atomic level is fundamental in the initial phases of the drug development process. Molecular dynamics simulations and complementary computational methods are already contributing in this endeavor for the soluble pharmacological targets and show an increasing importance in the understanding of membrane-ligand interactions.

The decreased productivity of the pharmaceutical industry in terms of new medical entities approved by the US FDA and the European Medicines Agency (EMEA) on a yearly basis has long been debated. This review will analyze overall new drug applications (NDAs) approved by both the FDA and EMEA in 2007, with the aim of finding trends (also looking at the past) that can be used to predict what the future may be. After a general introduction to the regulatory terminology, NDA approvals in 2007 are divided into categories (new applications of old medicines, metabolites, enantiomers and prodrugs, biological products, natural products and small organic molecule new molecular entities) and discussed. General aspects of the NDA approvals, such as historical trends, the length of the drug-discovery process, geography, differences among therapeutic areas, and the relative role of biotech and pharma industries are also outlined. From this analysis, a perspective is gained on some aspects that will probably influence future drug approvals. The conclusion is that 2007 may represent an inflexion point, in terms of quality if not quantity of new approvals, and that the future may be brighter than previously forecast.