Innovation and greater probability of success in drug discovery and development — from target to biomarkers

Systems biology approach for new target and biomarker identification

The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.

Towards a more robust approach to selecting and prosecuting promising targets and compounds

There are many factors that influence predictivity in drug discovery and impact on productivity within the pharmaceutical industry. This article will concentrate on just two aspects; first, the role of investigating target modulation within a (human) disease setting, from target selection through screening to animal models, and second, potential developments in the analysis and probing of the chemical space appropriate for drug discovery and, in particular, steps to improve predictivity thus moving to a more forward-looking process. The activities associated with target selection should develop significantly over the next 5-10 years leading to a more robust association of target modulation with disease modification. In addition, better understanding of the opportunities for target modulators should drive and improve the selection of ligands suitable for therapeutic applications. Within these areas it will be important to move away from a retrospective consideration of druggable targets towards a forward-looking approach based on holistic (disease context) profiling of both (progressable) targets and subsequently their ligands. Improvements in the predictive analysis and probing of the chemical space will be needed to confront both safety and efficacy end points that currently remain major reasons for failure in the clinic. It is hoped that improvements in data visualization together with chemocentric mining of the literature will facilitate better interrogation of development and clinical data, potentially modifying research project plans to better address these key issues.

Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters' perspective

Innovation is essential for the identification of novel pharmacological therapies to meet the treatment needs of patients with psychiatric disorders. However, over the last 20 yr, in spite of major investments targets falling outside the classical aminergic mechanisms have shown diminished returns. The disappointments are traced to failures in the target identification and target validation effort, as reflected by the poor ability of current bioassays and animal models to predict efficacy and side-effects. Mismatch between disease biology and how psychiatric diseases are categorized has resulted in clinical trials of highly specific agents in heterogeneous patients, leading to variable treatment effects and failed studies. As drug hunters, one sees the opportunity to overhaul the pharmaceutical research and development (R&D) process. Improvements in both preclinical and clinical translational research need to be considered. Linking pharmacodynamic markers with disease biology should provide more predictive and innovative early clinical trials which in turn will increase the success rate of discovering new medicines. However, to exploit these exciting scientific discoveries, pharmaceutical companies need to question the conventional drug research and development model which is silo-driven, non-integrative across the confines of a company, non-disclosing across the pharmaceutical industry, and often independent from academia. This leads to huge redundancy in effort and lack of contextual learning in real time. Nevertheless, there are signs that drug discovery in the 21st century will see more intentional government, academic and industrial collaborations to overcome the above challenges that could eventually link mechanistic disease biology to segments of patients, affording them the benefits of rational and targeted therapy.

[Biomarkers: "Found in translation"]

Despite continued increase in global Pharma R & D expenditure, the number of innovative drugs obtaining market approval has declined since 1994. The pharmaceutical industry is now entering a crucial juncture where increasing rates of attrition in clinical drug development as well as increasing development timelines are impacted by external factors such as intense regulatory pricing and safety pressures, increasing sales erosion due to generics, as well as exponential increases in the costs of bringing a drug to market. Despite these difficulties, numerous opportunities exist such as multiple unmet medical needs, the increasing incidence of certain diseases such as Alzheimer's disease, cancer, diabetes and obesity due to demographic changes, as well as the emergence of evolving markets such as China, India, and Eastern Europe. Consequently, Pharma is now responding to this challenge by improving both the productivity and the innovation in its drug discovery and development pipelines. In this regard, the advent of new technologies and expertise such as genomics, proteomics, structural biology, and molecular informatics in an integrated systems biology approach also provides a powerful opportunity for Pharma to address some of these difficulties. The key features behind this new strategy imply a discovery process based on an improved understanding of the molecular mechanism of diseases and drugs, translational research that places the patient at the center of the research process, and the application of biomarkers throughout the discovery and development phases. Moreover, new paradigms are required to improve target validation and develop more predictive cellular and animal models of human pathologies, a greater capacity in informatics-based analysis, and, consequently, a greater access to the vast sources of accumulating biological data and its integrated analysis. In the present review, we will address some of these issues and in particular emphasize how the application of biomarkers could potentially lead to improved productivity, quality, and innovation in drug discovery and ultimately better and safer medicines with improved therapeutic efficacy in specific pathologies for targeted patients.

The state of innovation in drug development

The pharmaceutical industry is facing tremendous pressure, not only from payers but as a result of public perception, regulatory hurdles, and the intricacies of research and development (R&D). The latter two are significant in that they affect the number of drugs that may be registered by regulatory authorities, the time to discover and develop drugs, and the cost of drug development. Because drug development has been stagnant in terms of innovation, there exists huge potential for innovation. Failure to innovate drug development will render the "big pharma" model unsustainable.

Ultrafast shape recognition for similarity search in molecular databases

Molecular databases are routinely screened for compounds that most closely resemble a molecule of known biological activity to provide novel drug leads. It is widely believed that three-dimensional molecular shape is the most discriminating pattern for biological activity as it is directly related to the steep repulsive part of the interaction potential between the drug-like molecule and its macromolecular target. However, efficient comparison of molecular shape is currently a challenge. Here, we show that a new approach based on moments of distance distributions is able to recognize molecular shape at least three orders of magnitude faster than current methodologies. Such an ultrafast method permits the identification of similarly shaped compounds within the largest molecular databases. In addition, the problematic requirement of aligning molecules for comparison is circumvented, as the proposed distributions are independent of molecular orientation. Our methodology could be also adapted to tackle similar hard problems in other fields, such as designing content-based Internet search engines for three-dimensional geometrical objects or performing fast similarity comparisons between proteins. From a broader perspective, we anticipate that ultrafast pattern recognition will soon become not only useful, but also essential to address the data explosion currently experienced in most scientific disciplines.

Ultrafast shape recognition to search compound databases for similar molecular shapes

Finding a set of molecules, which closely resemble a given lead molecule, from a database containing potentially billions of chemical structures is an important but daunting problem. Similar molecular shapes are particularly important, given that in biology small organic molecules frequently act by binding into a defined and complex site on a macromolecule. Here, we present a new method for molecular shape comparison, named ultrafast shape recognition (USR), capable of screening billions of compounds for similar shapes using a single computer and without the need of aligning the molecules before testing for similarity. Despite its extremely fast comparison rate, USR will be shown to be highly accurate at describing, and hence comparing, molecular shapes.