221st ACS. Division of Medicinal Chemistry - first-time disclosure of clinical candidates

The 221st ACS meeting in San Diego, USA, saw a symposium entitled 'First-time disclosure of clinical candidates', the inaugural presentation of this symposium in the society's 125-year history. Dr Balu N Balasubramanian (Bristol-Myers Squibb Co, USA), Chair of this well-attended symposium of five papers, remarked that he hoped that this symposium will become a regular event at ACS conferences.     

Screening for human ADME/Tox drug properties in drug discovery

There is no doubt that ADME/Tox drug properties, absorption, distribution, metabolism, elimination and toxicity, are properties crucial to the final clinical success of a drug candidate. It has been estimated that nearly 50% of drugs fail because of unacceptable efficacy, which includes poor bioavailability as a result of ineffective intestinal absorption and undesirable metabolic stability(1). It has also been estimated that up to 40% of drug candidates have failed in the past because of safety issues(2). In this review, the methodologies that are available for use in drug development as in vitro human-based screens for ADME/Tox drug properties are discussed.     

The emerging importance of predictive ADME simulation in drug discovery.

Absorption, distribution, metabolism and excretion (ADME) studies, are widely used in drug discovery to optimize the balance of properties necessary to convert leads into good medicines. However, throughput using traditional methods is now too low to support recent developments in combinatorial and library chemistry, which have generated many more molecules of interest. To the more enlightened practitioners of ADME science, this situation is generating both the problem and the solution: an opportunity is now forming, with the use of higher throughput ADME screens and computational models, to access this wide chemical diversity and to dissect out the rules that dictate a pharmacokinetic or metabolic profile. In the future we could see ADME properties designed-in from the first principles in drug design.     

ADME/PK as part of a rational approach to drug discovery

Rational drug discovery requires an early appraisal of all factors impacting on the likely success of a drug candidate in the subsequent preclinical, clinical and commercial phases of drug development. The study of absorption, distribution, metabolism, excretion and pharmacokinetics (ADME/PK) has developed into a relatively mature discipline in drug discovery through the application of well-established in vitro and in vivo methodologies. The availability of improved analytical and automation technologies has dramatically increased our ability to dissect out the fundamentals of ADME/PK through the development of increasingly powerful in silico methods. This is fuelling a shift away from the traditional, empirical nature of ADME/PK towards a more rational, in cerebro approach to drug design.     

Medicinal Chemistry--XVth EFMC International Symposium. New technologies in drug discovery. 6-10 September 1998, Edinburgh, Scotland, UK

This report summarizes the new technologies in drug discovery session of a five-day symposium organized by the Royal Society of Chemistry, Perkin Division, and the Biological and Medicinal Chemistry Sector of the Industrial Affairs Division on behalf of the European Federation of Medicinal Chemistry. The objective of this international symposium, which was attended by over 1000 participants from 48 different countries, was to appeal to all scientists interested in drug discovery from lead identification to lead optimization. The session on new technologies focused on those tools currently being used in these processes, with particular emphasis on new developments. There were seven supporting sessions on specific molecular target classes and one on the prediction of drug metabolism and pharmacokinetics. In addition, there were over 350 supporting posters held in two separate sessions.     

In vitro ADME phenotyping in drug discovery: current challenges and future solutions

Drug-metabolizing enzymes and drug transporters are key regulators of drug disposition and pharmacodynamics, which are closely linked to drug efficacy and safety. In this article, current challenges and future solutions to predicting their influence on pharmacokinetics and inter-organ distribution in humans, from data generated during the drug discovery decision-making process, are presented. In vitro phenotyping strategies for drug metabolizing enzymes (eg, CYP3A4, UGT1A1) and transporters (eg, OATP1B1) are offered, including perspectives on a selection of in vitro systems, novel in vitro phenotyping reagents and remaining technology gaps, challenges in extrapolating in vitro data to the in vivo situation, in silico models for the prediction of whether compounds are enzyme or transporter substrates, and the impact of pharmacogenomics.     

A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates

Over the years, multiple in silico solutions have been developed for the early characterisation of lead candidates at early stages of the drug development process. Despite the nascent promise this technology holds for the pharmaceutical and biotech industries, in many cases, inherent limitations in many of these computational technologies still hinders the prediction performance of absorption, distribution, metabolism and excretion (ADME), and toxicological (Tox) properties. However, as the result of recent developments in this arena and key technology collaborations, Bio-Rad Laboratories, Inc. has made some breakthroughs with their in silico ADME/Tox prediction and lead optimisation solutions. The company's KnowItA11 ADME/Tox system, when used in conjunction with Equbits' Foresight support vector machine platform and other best-of-breed partnering technologies, provides an intelligent and flexible approach to in silico modelling that helps to overcome these difficulties. The system ultimately does this by offering various approaches and technologies that can lead researchers toward improvement in results and overall greater confidence in the in silico approach as a whole. In this technology evaluation, several examples and case studies on mutagenicity and hERG-channel blocking illustrate how researchers can take advantage of this system from compound characterisation to knowledge extraction to achieve better and faster results in their research process.     

Early ADME in support of drug discovery: the role of metabolic stability studies

The area of Drug Discovery has undergone an amazing evolution in the past decade. This evolution is typified by the development of automated combinatorial synthesis and high throughput pharmacological testing. This, in turn, has lead to the ability to create and mine extensive databases and then model this new information. The overall result is a substantial increase in the rate of target identification, generation of new leads, and finally, optimization of those leads into clinical candidates. ADME studies have always played a critical role in helping to optimize the pharmacokinetic (PK) properties of new drugs thereby increasing their success rate. As a consequence of the increased throughput of drug discovery, ADME studies have evolved to keep pace. These so-called "early ADME" studies, are characterized by parallel processing and higher throughput than before. A primary concern of medicinal chemists is to design molecules that will have not only the desired activity, but also suitable potency and duration of action, which is influenced by pharmacokinetic properties such as bioavailability and half-life. This article focuses on a particular subset of eADME studies known as "metabolic stability", which can be an important contributor for a good pharmacokinetic profile. Metabolic stability studies represent the adaptation of more complex metabolism rate studies to a minimized system suitable for parallel processing of large numbers of compounds. The theoretical basis for metabolic stability lies in its relationship to the concept of metabolic intrinsic clearance. Typical metabolic stability protocols are discussed with respect to their relation to drug design. How metabolic stability studies have evolved to keep pace with advances in drug discovery is also discussed. Several case studies of the role of metabolic stability in drug design over the past few years are summarized to exemplify the utility of this kind of study. Finally, future trends in drug metabolism and analytical chemistry and how they may influence metabolic stability studies are reviewed.     

In-silico ADME models: a general assessment of their utility in drug discovery applications

ADME prediction is an extremely challenging area as many of the properties we try to predict are a result of multiple physiological processes. In this review we consider how in-silico predictions of ADME processes can be used to help bias medicinal chemistry into more ideal areas of property space, minimizing the number of compounds needed to be synthesized to obtain the required biochemical/physico-chemical profile. While such models are not sufficiently accurate to act as a replacement for in-vivo or in-vitro methods, in-silico methods nevertheless can help us to understand the underlying physico-chemical dependencies of the different ADME properties, and thus can give us inspiration on how to optimize them. Many global in-silico ADME models (i.e generated on large, diverse datasets) have been reported in the literature. In this paper we selectively review representatives from each distinct class and discuss their relative utility in drug discovery. For each ADME parameter, we limit our discussion to the most recent, most predictive or most insightful examples in the literature to highlight the current state of the art. In each case we briefly summarize the different types of models available for each parameter (i.e simple rules, physico-chemical and 3D based QSAR predictions), their overall accuracy and the underlying SAR. We also discuss the utility of the models as related to lead generation and optimization phases of discovery research.