Combinatorial Chemistry & High Throughput Screening - Volume 13, Issue 2, 2010

The nomination of a lead discovery molecule to a viable drug candidate is a key step in the drug discovery/development continuum. Transition through this critical milestone requires the knowledge of the pharmacological action as well as insights into the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of the compound. The current motivation to balance the pharmacological attributes of a drug candidate with ADMET characteristics is reflective of multiple surveys conducted over the years which examined the root sources for candidate failure. Interestingly independent of the era examined, the bottom-line for the pharmaceutical industry is that about half of the root causes for failure of NCEs during drug development were attributed to poor pharmacokinetics, ADME or safety-related properties associated with the molecule. In response to this condition, ADMET screening for the past decade has evolved to become a fixture in most large and small pharmaceutical company drug discovery strategies. Over this time, pharmaceutical discovery candidate profiling has evolved, not only in developing the technical foundation of predictive ADMET diagnostic tools, but also towards integrating these tools into drug discovery decision making. However despite, the recognition of the need for ADMET screening in drug discovery, the full value proposition of this investment has yet to be realized. In many cases, the element which may be confounding the full potential of these ADMET screens from impacting drug candidate success is not associated with the throughput of a particular assay, or even the robustness of the data generated, but rather how well the discovery team aggregates all the various incoming streams of information into one cohesive information package which is suitable to drive decision making around candidate selection. A working hypothesis in this area reflects the notion that a comprehensive exploitation of PK/ADMET discovery data, instead of iteratively focusing on a single parameter, can enhance the success rate of drug discovery candidates. A caveat to this idea is that the knowledge of the assay cannot be privately held by a single stake holder; restated, in order to achieve success, drug discovery teams will need to understand and appreciate the various PK/ADMET concepts and limitations to make full and adequate uses of these studies to prioritize the candidates. To this end, this issue of Combinatorial Chemistry & High Throughput Screening has sought to assemble a collection of reviews on the application of PK/ADMET screens in drug discovery. The structure of this issue is organized in a manner which reflects the flow of activities that underwrite the introduction of a small molecule into systemic circulation and the associated areas of concern for a drug discovery candidate beyond its pharmacological activity (Scheme 1).

Physicochemical properties of drug molecules impact many aspects of both in vivo and in vitro behavior. Poor physicochemical properties can often create a significant impediment to establishing reliable SAR, establishing proof of principle type studies using in vivo models, and eventually leading to added performance variability and costs throughout the development life cycle; in the worst case scenario, even preventing execution of the desired development plan. Understanding the fundamental physicochemical properties provides the basis to dissect and deconvolute experimental observations in such a way that modification or mitigation of poor molecular properties can be impacted at the design phase, insuring design and selection of a molecule which has a high probability of making it through the arduous development cycle. This review will discuss the key physicochemical properties and how they can be assessed and how they are implicated in both discovery enablement and in final product developability of the selected candidate.

Solubility and cellular permeability are two of the most important biopharmaceutical properties impacting the successful development of drug substances. Given the importance of these properties, most pharmaceutical companies have invested in medium to high throughput technologies for early evaluation of these characteristics in the drug discovery funnel in order to select, prioritize or eliminate compounds with unfavorable solubility and/or permeability. However, these technologies require physical samples of the substances to be tested. In order to facilitate the early stages of drug discovery, such as defining compound collection composition, designing combinatorial libraries, and in hit expansion or lead optimization, models for predicting aqueous solubility and permeability in the absence of physical sample are increasingly being employed. In this overview, we will discuss solubility and permeability experimental and computational methods separately and then interrelate them in physiologically relevant models for predicting in vivo performance.

Drug transporters play an important role in the absorption, distribution, excretion and toxicity of both endogenous and exogenous compounds. Transporters may act as physiological ‘gatekeepers’ in the regulation of the pharmacological and/or toxicological effects of drugs by limiting distribution to tissues responsible for their effect and/or toxicity. This review will first provide a brief outline of the characteristics of membrane bound drug transporter families and their respective roles in regulating drug pharmacokinetics. This background then provides the context for a discussion of the characterization of a drug candidate as a substrate, inhibitor and/or inducer of drug transporter(s), followed by an assessment of the in vitro and in vivo preclinical methods used in drug discovery and development for screening molecules to identify potential transporter interactions. Finally, specific examples of the translation of in vitro findings to the in vivo effects are discussed to link the current understanding of the impact of drug transporters to clinical pharmacology. Thus, the goal is to provide the drug discovery scientist with a cadre of concepts, strategies, and tools for ultimately making rational decisions in drug design and delivery resulting in the optimization of drug concentrations at the target of pharmacology.

Drug metabolizing enzyme induction, or the process of generating excess drug metabolizing enzyme in important tissues of drug disposition such as liver and intestine, can give rise to pharmacokinetic situations whereby drug interactions occur. There are two major concerns associated with enzyme induction. First is the potential loss of efficacy due to more rapid metabolism and second is the risk of an increase in the formation of a potential reactive/toxic metabolite. Because of this, pharmaceutical companies consider enzyme induction as an undesired drug property for their potential drug candidates. As a consequence, the number of tools and models to evaluate induction of drug metabolizing enzymes has been increased tremendously over the past decade. As often is the case, every assay and approach has its own advantages and disadvantages and unfortunately, no single tool is currently available to predict the induction potential of drug candidates in humans. The purpose of this review is not only to outline the screening tools currently available for determining the induction potential of new chemical entities but also to translate these tools into a valuable screening strategy, covering aspects such as, induction liability assessment, structure-activity relationships (SAR), induction risk assessment and translating in vitro findings into clinical relevance.

Advances in drug interaction screening have resulted in reduced compound attrition rates due to unfavorable CYP-mediated drug interactions in clinical trials and improved patient safety. A major driver for the success in predicting drug interactions is a better understanding of the biological, chemical or mechanical factors that can impact the prediction of drug interactions in vitro. The enzyme source, probe substrate, accessory proteins and pharmacogenetics can all have profound effects upon the robustness and relevance of data generated with in vitro drug-drug interaction assays. Furthermore, the use of in silico techniques can potentially afford a priori knowledge of drug interaction potential, thus reducing the time and cost associated with drug interaction screening. This review will focus on recent advances in in vitro, in silico and bioanalytical techniques and demonstrate how these tools are currently used to provide effective CYP drug interaction screening in a discovery setting.

In this review, the current approach to predicting hepatic clearance from in vitro metabolic systems is discussed along with a survey of current industry practice. The definitive method of determining intrinsic clearance remains the measurement of Michaelis-Menten parameters derived from metabolite formation rate data. However, in drug discovery this method has limitations which result in the method most commonly applied being the half-life method utilizing a single, low substrate concentration. Additionally, the importance of correcting in vitro intrinsic clearance values for futile binding within the incubation has become accepted, although the majority of the respondents to the industry survey do not currently correct for this. It is also apparent that most investigators employ standard incubation conditions for determining intrinsic clearance which may not be appropriate for all compounds. Adapting these conditions to vary both the substrate and enzyme concentrations offers a relatively simple method to gain useful information regarding potential Km values and in vitro futile binding. Although there are many factors that influence the relationship between intrinsic clearance and hepatic clearance, even in a discovery setting opportunities exist for limited tailoring of assay conditions, which when combined with some judicious assumptions and forethought, make it possible to use intrinsic clearance values in a rational manner to identify compounds with the desired pharmacokinetic properties.

This review describes methods for quantifying the binding of small molecule drug candidates to plasma proteins and the application of these methods in drug discovery and development. Particular attention is devoted to methods amenable to medium-to-high throughput analysis and those well suited for measurement of compounds that are highly protein bound. The methods reviewed herein include the conventional techniques of equilibrium dialysis, ultrafiltration and ultracentrifugation, as well as some more novel approaches utilizing micropartitioning and biosensorbased analysis. Additional concepts that are discussed include plasma protein structure, enantioselective protein binding, drug displacement, the effect of patient demographics and disease states on free (unbound) drug levels, and the influence of protein binding on drug candidate pharmacokinetics and pharmacodynamics. Practical considerations pertaining to the evaluation of highly protein bound drug candidates are also highlighted.

One of the greatest challenges facing the pharmaceutical industry today is the failure of promising new drug candidates due to unanticipated adverse effects discovered during preclinical animal safety studies and clinical trials. Late stage attrition increases the time required to bring a new drug to market, inflates development costs, and represents a major source of inefficiency in the drug discovery/development process. It is generally recognized that early evaluation of new drug candidates is necessary to improve the process. Building in vitro data sets that can accurately predict adverse effects in vivo would allow compounds with high risk profiles to be deprioritized, while those that possess the requisite drug attributes and a lower risk profile are brought forward. In vitro cytotoxicity assays have been used for decades as a tool to understand hypotheses driven questions regarding mechanisms of toxicity. However, when used in a prospective manner, they have not been highly predictive of in vivo toxicity. Therefore, the issue may not be how to collect in vitro toxicity data, but rather how to translate in vitro toxicity data into meaningful in vivo effects. This review will focus on the development of an in vitro toxicity screening strategy that is based on a tiered approach to data collection combined with data interpretation.

A goal of preclinical discovery is the identification of drug candidates suitable for clinical testing. Successful integration of in vitro and in vivo experimental data sets can afford projections of human dose regimens anticipated to be safe and therapeutically beneficial. While in vitro experiments guide new chemical syntheses and are essential to understanding drug action and disposition, in vivo characterizations provide unique insight into complex biological systems that control concentrations at the site of action and pharmacologic response. Pharmacokinetic and pharmacodynamic (PK/PD) concepts underlying drug disposition and response provide a quantitative framework with which to identify potential clinical candidates. To improve throughput in earlier stages of drug discovery, in vivo pharmacokinetic study designs such as cassette dosing and sparse sampling schemes have been utilized. In later stages of discovery, pharmacokinetic studies using chemical inhibitors or surgical and genetic animal models are used to characterize the underlying determinants of drug disposition. In a complimentary fashion, modeling of in vivo pharmacodynamic effects may quantitatively link biomarkers to pharmacological response, validate in vitro to in vivo correlations and underwrite predictions of efficacious exposure targets. When applied to in vivo discovery data, PK/PD models have aided in understanding mechanisms of pharmacological response such as receptor theory in the central nervous system and cell turnover concepts in infectious disease and oncology. This review considers the role of in vivo testing toward understanding the pharmacokinetic and pharmacodynamic attributes of lead candidates in drug discovery.

Larry C. Wienkers is currently the Executive Director for the Department of Pharmacokinetics and Drug Metabolism at Amgen in Seattle, WA. Larry's areas of scientific interest include: understanding of the mechanisms of cytochrome P450 oxidation reactions; the application of novel in vitro metabolism techniques to understand the enzymatic basis for biotransformation of potential therapeutic agents at the drug discovery interface; and the prospective application of in vitro metabolism data to predict clinically relevant pharmacokinetically based drug-drug interactions. Larry holds a BSc. and MS. in Chemistry from Western Washington University (1986 & 1988) and Ph.D. in Medicinal Chemistry from the University of Washington (1993). Larry worked as a Postdoctoral Fellow, in the Department of Drug Metabolism at the Upjohn Company (1993-1995) and he joined Pharmacia and Upjohn as a Research Scientist (1995). Larry became Director of Drug Metabolism Enabling Technologies and Scientific Fellow at Pharmacia, Kalamazoo MI (1998). In 2003, Larry became Executive Director in the Department of Pharmacokinetics, Dynamics and Metabolism at Pfizer St Louis, MO. Larry has co-authored more than 50 peer reviewed manuscripts, 4 book chapters and currently holds 1 patent. He is co-Editor of the Handbook of Drug Metabolism, and is a member of the Editorial Board for the journals: Drug Metabolism and Disposition, Open Medicinal Chemistry Letters, and Current Drug Metabolism.