Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.     

Present and future in vitro approaches for drug metabolism

The 1980s through 1990s witnessed the widespread incorporation of in vitro absorption, distribution, metabolism, and excretion (ADME) approaches into drug development by drug companies. This has been exemplified by the integration of the basic science of cytochrome P450s (CYPs) into most drug metabolism departments so that information on the metabolic pathways of drugs and drug-drug interactions (DDIs) is no longer an academic exercise, but essential for regulatory submission. This has come about due to the application of a variety of new technologies and in vitro models. For example, subcellular fractions have been widely used in metabolism studies since the 1960s. The last two decades has seen the increased use of hepatocytes as the reproducibility of cell isolations improved. The 1990s saw the rejuvenation of liver slices (as new slicers were developed) and the utilization of cDNA expressed enzymes as these technologies matured. In addition, there has been considerable interest in extrapolating in vitro data to in vivo for parameters such as absorption, clearance and DDIs. The current philosophy of drug development is moving to a 'fail early--fail cheaply' paradigm. Therefore, in vitro ADME approaches are being applied to drug candidates earlier in development since they are essential for identifying compounds likely to present ADME challenges in the latter stages of drug development. These in vitro tools are also being used earlier in lead optimization biology, in parallel with approaches for optimizing target structure activity relationships, as well as identification of DDI and the involvement of metabolic pathways that demonstrate genetic polymorphisms. This would suggest that the line between discovery and development drug metabolism has blurred. In vitro approaches to ADME are increasingly being linked with high-throughput automation and analysis. Further, if we think of perhaps the fastest available way to screen for successful drugs with optimal ADME characteristics, then we arrive at predictive computational algorithms, which are only now being generated and validated in parallel with in vitro and in vivo methods. In addition, as we increase the number of ADME parameters determined early, the overall amount of data generated for both discovery and development will increase. This will present challenges for the efficient and fast interpretation of such data, as well as incorporation and communication to chemistry, biology, and clinical colleagues. This review will focus on and assess the nature of present in vitro metabolism approaches and indicate how they are likely to develop in the future.     

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.     

The impact of informatics and computational chemistry on synthesis and screening

High-throughput synthesis and screening technologies have enhanced the impact of computational chemistry on the drug discovery process. From the design of targeted, drug-like libraries to ‘virtual’ optimization of potency, selectivity and ADME/Tox properties, computational chemists are able to efficiently manage costly resources and dramatically shorten drug discovery cycle times. This review will describe some of the successful strategies and applications of state-of-the-art algorithms to enhance drug discovery, as well as key points in the drug discovery process where computational methods can have, and have had, greatest impact.     

Progress in predicting human ADME parameters in silico

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug-drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.     

Computer-assisted methods in chemical toxicity prediction

In Silico predictive ADME/Tox screening of compounds is one of the hottest areas in drug discovery. To provide predictions of compound drug-like characteristics early in modern drug-discovery decision making, computational technologies have been widely accepted to develop rapid high throughput in silico ADMET analysis. It is widely perceived that the early screening of chemical entities can significantly reduce the expensive costs associated with late stage failures of drugs due to poor ADME/Tox properties. Drug toxic effects are broadly defined to include toxicity, mutagenicity, carcinogenicity, teratogenicity, neurotoxicity and immunotoxicity. Toxicity prediction techniques and structure-activity relationships relies on the accurate estimation and representation of physico-chemical and toxicological properties. This review highlights some of the freely and commercially available softwares for toxicity predictions. The information content can be utilized as a guide for the scientists involved in the drug discovery arena.     

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.     

Permeability--in vitro assays for assessing drug transporter activity

The accumulating evidence has revealed that drug transporters have essential roles in the delivery and excretory processes of drugs and their metabolites. Inhibition or induction of drug transporters can affect pharmacokinetic properties and therapeutic efficacy of a drug. Thus, the characterization of drug-transporter interactions becomes important for the selection of compounds to avoid transporter associated absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) issues. Additionally, the potential use of absorptive transporters for drug delivery has been recognized for drug design. In vitro and in vivo approaches have been developed for studying the transporter activities. In vitro assays can rapidly provide the information for identifying interaction of a compound and a particular transporter and have proved to be amenable to high throughput approaches. Therefore, the studies are conducted in early drug discovery. In this article, in vitro methods are reviewed, including cell free and cell-based assays. Their applications, limitations and impact on drug discovery are discussed.     

Techniques: application of systems biology to absorption, distribution, metabolism, excretion and toxicity

It is widely recognized that either predicting or determining the absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties of molecules helps to prevent the failure of some compounds before they reach the clinic. Consequently, there has been considerable research into developing better in silico, in vitro and in vivo methods and models. Toxicogenomics, proteomics, metabonomics and pharmacogenomics represent the latest experimental approaches that can be combined with high-throughput molecular screening of targets to provide a view of the complete biological system that is modulated by a compound. The functional interpretation and relevance of these complex multidimensional data to the phenotype observed in humans is the focus of current research in toxicology. Multiple content databases, data mining and predictive modeling algorithms, visualization tools, and high-throughput data-analysis solutions are being integrated to form systems-ADME/Tox. In this review, we focus on the most recent advances and applications in this area.     

Application of advanced in silico methods for predictive modeling and information integration

INTRODUCTION: In silico predictive methods are well-known tools to the drug discovery process. In recent years, these tools have become of strategic interest to regulatory authorities to support risk-based approaches and to complement, and potentially strengthen evidence when considering product quality and safety of human pharmaceuticals. AREAS COVERED: This editorial reviews how chemically intelligent systems and computational models using structure-based assessments are important for providing predictive data on drug toxicity and safety liabilities considered at the FDA. The example of regulatory interest in application of in silico systems for mutagenicity predictions of drug impurities is discussed. EXPERT OPINION: The importance of information integration is emphasized toward the application of in silico predictive methods and enhancing data mining capabilities for safety signal detection. Modeling for cardiovascular drug safety based on human clinical trial data is one area of active testing of predictive technologies at the FDA. The FDA has taken appropriate steps in its strategies and initiatives aimed to enhance and support innovation for regulatory science and medical product development by developing and implementing the use of in silico predictive models and medical toxicity databases. This science priority area will ultimately help improve and protect public health.     

In silico prediction of ADME and pharmacokinetics: Report of an expert meeting organised by COST B15

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are 'open' systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches.     

Virtual screening and its integration with modern drug design technologies

Drug discovery is a highly complex and costly process, which demands integrated efforts in several relevant aspects involving innovation, knowledge, information, technologies, expertise, R&D investments and management skills. The shift from traditional to genomics- and proteomics-based drug research has fundamentally transformed key R&D strategies in the pharmaceutical industry addressed to the design of new chemical entities as drug candidates against a variety of biological targets. Therefore, drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein-ligand interactions. The combination of available knowledge of several 3D protein structures with hundreds of thousands of small-molecules have attracted the attention of scientists from all over the world for the application of structure- and ligand-based drug design approaches. In this context, virtual screening technologies have largely enhanced the impact of computational methods applied to chemistry and biology and the goal of applying such methods is to reduce large compound databases and to select a limited number of promising candidates for drug design. This review provides a perspective of the utility of virtual screening in drug design and its integration with other important drug discovery technologies such as high-throughput screening (HTS) and QSAR, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.     

基于系统生物学的药物毒理学研究进展

近年来涉及系统生物学的毒理学研究已日渐兴起,在整体性、动态性、网络调控性的内涵下,关注外来物质对机体的损伤评估与预测。药物与生物大分子作用涉及机体网络系统的巨大复杂性,使得对药物毒性作用机制的理解难度也有所增加。应用计算与实验系统生物学,药物毒理学研究在生物组织扩大到多重尺度网络分析,并由此说明治疗作用和不良反应。系统毒理学依靠实验"组学"技术,在大量可变因素中能测量多重变化,通常在全基因组水平建立网络分析药物作用。组学技术由于将个体基因组状态联系到所用药物的治疗效能和毒性反应,通常在全基因组水平建立分析药物毒性的网络系统。通路与网络分析相结合,毒理效应与毒代动力学模型,和基因多态性知识,将发展为预测毒性作用的模型。基于诸如美国食品药品管理局不良事件报告系统网络分析,可建立初步了解分子水平的药物靶点相互作用,导致器官和各级水平不良反应表型过程的远端效应。系统生物学的集成数据设计分子及相互之间作为一个网络行为,如动力学模拟,代谢调控,鲁棒性和流量分析,确实有助于理解网络介导的毒性及药物毒理学。     

QSAR and QM/MM approaches applied to drug metabolism prediction

In modern drug discovery process, ADME/Tox properties should be determined as early as possible in the test cascade to allow a timely assessment of their property profiles. To help medicinal chemists in designing new compounds with improved pharmacokinetics, the knowledge of the soft spot position or the site of metabolism (SOM) is needed. In recent years, large number of in silico approaches for metabolism prediction have been developed and reported. Among these methods, QSAR models and combined quantum mechanics/molecular mechanics (QM/MM) methods for predicting drug metabolism have undergone significant advances. This review provides a perspective of the utility of QSAR and QM/MM approaches on drug metabolism prediction, highlighting the present challenges, limitations, and future perspectives in medicinal chemistry.     

Spectroscopic and statistical methods in metabonomics

Metabonomics is rapidly evolving through advances in analytical technologies together with the development of new hyphenated approaches that are increasingly being applied to analyze complex biological systems. Improvements in analytical performance, such as increased sensitivity and selectivity, are providing greater resolution to analytical datasets and the rich potential of metabonomics as a systems biology tool of choice is becoming clear. However, such improvements are resulting in datasets becoming increasingly demanding in terms of data handling and interpretation, and the degree to which metabonomics continues to develop will be dependent on how chemometrics and data-handling approaches keep pace with continually improving analytical technologies. This review provides an overview of the field of metabonomics, with a particular focus on the analytical techniques that are chiefly employed and the chemometric methods that have found most use. However, in addition, we mention less widely used analytical methods and suggest that advanced statistical methods will play a larger role in the future.     

Information management systems for pharmacogenomics

The value of high-throughput genomic research is dramatically enhanced by association with key patient data. These data are generally available but of disparate quality and not typically directly associated. A system that could bring these disparate data sources into a common resource connected with functional genomic data would be tremendously advantageous. However, the integration of clinical and accurate interpretation of the generated functional genomic data requires the development of information management systems capable of effectively capturing the data as well as tools to make that data accessible to the laboratory scientist or to the clinician. In this review these challenges and current information technology solutions associated with the management, storage and analysis of high-throughput data are highlighted. It is suggested that the development of a pharmacogenomic data management system which integrates public and proprietary databases, clinical datasets, and data mining tools embedded in a high-performance computing environment should include the following components: parallel processing systems, storage technologies, network technologies, databases and database management systems (DBMS), and application services.     

Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research

Systems chemical biology, the integration of chemistry, biology and computation to generate understanding about the way small molecules affect biological systems as a whole, as well as related fields such as chemogenomics, are central to emerging new paradigms of drug discovery such as drug repurposing and personalized medicine. Recent Semantic Web technologies such as RDF and SPARQL are technical enablers of systems chemical biology, facilitating the deployment of advanced algorithms for searching and mining large integrated datasets. In this paper, we aim to demonstrate how these technologies together can change the way that drug discovery is accomplished.