Recent advances in computational prediction of drug absorption and permeability in drug discovery

Approximately 40%-60% of developing drugs failed during the clinical trials because of ADME/Tox deficiencies. Virtual screening should not be restricted to optimize binding affinity and improve selectivity; and the pharmacokinetic properties should also be included as important filters in virtual screening. Here, the current development in theoretical models to predict drug absorption-related properties, such as intestinal absorption, Caco-2 permeability, and blood-brain partitioning are reviewed. The important physicochemical properties used in the prediction of drug absorption, and the relevance of predictive models in the evaluation of passive drug absorption are discussed. Recent developments in the prediction of drug absorption, especially with the application of new machine learning methods and newly developed software are also discussed. Future directions for research are outlined.     

Advances in computationally modeling human oral bioavailability

Although significant progress has been made in experimental high throughput screening (HTS) of ADME (absorption, distribution, metabolism, excretion) and pharmacokinetic properties, the ADME and Toxicity (ADME–Tox) in silico modeling is still indispensable in drug discovery as it can guide us to wisely select drug candidates prior to expensive ADME screenings and clinical trials. Compared to other ADME–Tox properties, human oral bioavailability (HOBA) is particularly important but extremely difficult to predict. In this paper, the advances in human oral bioavailability modeling will be reviewed. Moreover, our deep insight on how to construct more accurate and reliable HOBA QSAR and classification models will also discussed.     

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.     

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.     

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.     

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.     

Systems-ADME/Tox: resources and network approaches

The increasing cost of drug development is partially due to our failure to identify undesirable compounds at an early enough stage of development. The application of higher throughput screening methods have resulted in the generation of very large datasets from cells in vitro or from in vivo experiments following the treatment with drugs or known toxins. In recent years the development of systems biology, databases and pathway software has enabled the analysis of the high-throughput data in the context of the whole cell. One of the latest technology paradigms to be applied alongside the existing in vitro and computational models for absorption, distribution, metabolism, excretion and toxicology (ADME/Tox) involves the integration of complex multidimensional datasets, termed toxicogenomics. The goal is to provide a more complete understanding of the effects a molecule might have on the entire biological system. However, due to the sheer complexity of this data it may be necessary to apply one or more different types of computational approaches that have as yet not been fully utilized in this field. The present review describes the data generated currently and introduces computational approaches as a component of ADME/Tox. These methods include network algorithms and manually curated databases of interactions that have been separately classified under systems biology methods. The integration of these disparate tools will result in systems-ADME/Tox and it is important to understand exactly what data resources and technologies are available and applicable. Examples of networks derived with important drug transporters and drug metabolizing enzymes are provided to demonstrate the network technologies.     

The use of glutathione transferase-knockout mice as pharmacological and toxicological models

ADME/Tox studies are of increasing importance because of the necessity to eliminate poor drug candidates early in the development pipeline. The glutathione S-transferases (GSTs) are a family of phase II enzymes that have been shown to play a significant role in the disposition of a wide range of drugs and other xenobiotics. Several GST-knockout mice strains have been developed that can potentially be used in ADME/Tox studies. So far, mice have been generated with deficiencies of mGSTP1/2, mGSTA4-4, mGSTZ1-1, mGSTM1-1, mGSTO1-1 and mGSTS1-1, but studies of drug metabolism in these strains have been limited. As there are 21 recognised GST genes in mice there is potential for many more strains to be made. However, a review of the available data suggests that because of differences in the evolution of the GST gene family between rodents and humans, only some knockout strains can provide insights relevant to human drug metabolism. It is concluded that, of the strains generated so far, only those deficient in mGSTP1-1, mGSTA4-4, mGSTO1-1 and mGSTZ1-1 have direct human orthologues and can be considered as human models. In contrast, there may not be appropriate orthologues of some enzymes, such as hGSTM1-1, that are known to be of relevance in drug metabolism.     

Mass spectrometry in drug discovery: a current review

Drug discovery in the pharmaceutical industry has shown great demands for screening absorption, distribution, metabolism, excretion (ADME) and pharmacokinetics (PK) in guiding the selection of lead candidate compounds. Determination of ADME/PK properties of new chemical entities (NCE) in early drug discovery should allow defects to be corrected prior to time-consuming and expensive preclinical and clinical development stages. Mass spectrometry has evolved to become an irreplaceable technology in all types of drug discovery applications because of its high sensitivity, speed, selectivity, versatility, and ease of automation. This review will include current mass spectrometric techniques and applications in drug discovery, as well as future prospects.     

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.     

Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection

The high-throughput screening in drug discovery for absorption, distribution, metabolism and excretion (ADME) properties has become the norm in the industry. Only a few years ago it was ADME properties that were attributed to more failure of drugs than efficacy or safety in the clinic trials. With the realization of new techniques and refinement of existing techniques better projections for the pharmacokinetic properties of compounds in humans are being made, shifting the drug failure attributes more to the safety and efficacy properties of drug candidates. There are a tremendous number of tools available to discovery scientists to screen compounds for optimization of ADME properties and selection of better candidates. However, the use of these tools has generally been to characterize these compounds rather than to select among them. This report discusses applications of the available ADME tools to better understand the clinical implication of these properties, and to optimize these properties. It also provides tracts for timing of studies with respect to the stage of the compound during discovery, by means of a discovery assay by stage (DABS) paradigm. The DABS provide the team with a rationale for the types of studies to be done during hit-to-lead, early and late lead optimization stages of discovery, as well as outlining the deliverables (objectives) at those stages. DABS has proven to be optimal for efficient utilization of resources and helped the discovery team to track the progress of compounds and projects.     

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.     

Development of an ADME and drug–drug interactions knowledge database for the acceleration of drug discovery and development

It is widely recognised that predicting or determining the absorption, distribution, metabolism and excretion (ADME) properties of a compound as early as possible in the drug discovery process helps to prevent costly late-stage failures. Although in recent years high-throughput in vitro absorption distribution metabolism excretion toxicity (ADMET) screens have been implemented, more efficient in silico filters are still highly needed to predict and model the most relevant metabolic and pharmacokinetic end points, and thereby accelerate drug discovery and development. The usefulness of the data generated and published for the chemist, biologist or project manager who ultimately wants to understand and optimise the ADME properties of lead compounds cannot be argued with. Collecting and comparing data is an overwhelming task for the time-pressed scientist. Aureus Pharma provides a uniquely specialised solution for knowledge generation in drug discovery. AurSCOPE^® ADME/DDI (drug–drug interaction) is a fully annotated, structured knowledge database containing all the pertinent biological and chemical information on the metabolic properties of drugs. This Aureus knowledge database has proven to be highly useful in designing predictive models and identifying potential drug–drug interactions.     

Biological fingerprinting analysis of traditional Chinese medicines with targeting ADME/Tox property for screening of bioactive compounds by chromatographic and MS methods

Traditional Chinese medicines (TCMs) are attracting increased global attention because of their potential to provide novel therapeutic agents based on substantial historical records of efficacy in man. Many strategies have been designed for the screening and selection of bioactive compounds from these complex natural products mixtures. Biological fingerprinting analysis (BFA), based on small molecule-biomacromolecule interactions in complex systems, has been applied to screen the multiple bioactive compounds in natural products. Here we review the chromatographic and MS approaches used for BFA of natural products with targeting absorption, distribution, metabolism, elimination and toxicity (ADME/Tox) properties. Such chromatographic methods cover a wide range of applications including liposome, serum proteins, liver homogenate and DNA profiling. MS methods for the characterization of molecular interactions between natural products and target molecules by ESI and MALDI-TOF MS are also discussed.     

Improving early drug discovery through ADME modelling: an overview

Drug development is an intrinsically risky business. Like a high stakes poker game the entry costs are high and the probability of winning is low. Indeed, only a tiny percentage of lead compounds ever reach US FDA approval. At any point during the drug development process a prospective drug lead may be terminated owing to lack of efficacy, adverse effects, excessive toxicity, poor absorption or poor clearance. Unfortunately, the more promising a drug lead appears to be, the more costly it is to terminate its development. Typically, the cost of killing a drug grows exponentially as a drug lead moves further down the development pipeline. As a result there is considerable interest in developing either experimental or computational methods that can identify potentially problematic drug leads at the earliest stages in their development. One promising route is through the prediction or modelling of ADME (absorption, distribution, metabolism and excretion). ADME data, whether experimentally measured or computationally predicted, provide key insights into how a drug will ultimately be treated or accepted by the body. So while a drug lead may exhibit phenomenal efficacy in vitro, poor ADME results will almost invariably terminate its development. This review focuses on the use of ADME modelling to reduce late-stage attrition in drug discovery programmes. It also highlights what tools exist today for visualising and predicting ADME data, what tools need to be developed, and the importance of integrating ADME data to aid in compound selection during the earliest phases of drug discovery. In particular, it highlights what tools exist today for visualising and predicting ADME data including: (1) ADME parameter predictors; (2) metabolic fate predictors; (3) metabolic stability predictors; (4) cytochrome P450 substrate predictors; and (5) physiology-based pharmacokinetic (PBPK) modelling software. It also discusses what kinds of tools need to be developed, and the importance of integrating ADME data to aid in compound selection during the earliest phases of drug discovery.     

Use of radioactive compounds and autoradiography to determine drug tissue distribution

Radioactivity has been used in drug discovery and development for several decades because it offers researchers a highly sensitive way to quantitatively assess the absorption, distribution, metabolism, and/or excretion (ADME) of chemical entities by incorporating a radioactive isotope into the structure of the drug molecule. Regulatory agencies around the world require drug makers to characterize the ADME properties of prospective new drugs as one way to help ensure that patients are not exposed to dangerous drug and/or drug metabolite levels before they can be approved for human use. Radiolabeled compounds have consistently proved to be the most efficient tool for determining that information, even though attempts have been made to use nonradioactive techniques. The techniques of quantitative whole-body autoradiography (QWBA) and microautoradiography (MARG), which rely on the use of radiolabeled drugs, are two techniques that are routinely used to examine tissue distribution of drugs in discovery and development. These techniques provide drug researchers with quantitative tissue concentration data and a visual location of those concentrations in intact organs, tissues, and cells of laboratory animals. It is important for readers to realize that these techniques visualize total radioactivity, which can include the parent molecule along with its metabolites, and/or degradation products or impurities. This requires investigators to treat the quantitative data with caution unless the identity of the radioactivity is determined using some type of other bioanalytical techniques, such as mass spectroscopy and/or radio-HPLC, which can be easily performed on the tissue obtained from the animals used for QWBA and/or MARG. Nevertheless, these data are used in drug discovery and development to answer questions related to tissue penetration, fetal/placental transfer, tissue retention, routes of elimination, drug-drug interactions, enzyme induction/inhibition, formulation comparisons, in vivo compound solubility, differential metabolite distribution, interspecies comparisons, and to predict human exposure to parent drugs, metabolites, and radiation during clinical studies. This review will consider the strategic use of WBA, QWBA, and MARG in the pharmaceutical industry. Case studies and anecdotal information will also be presented; however, readers should realize that these are general examples and that some details have been omitted for brevity and/or because the data is proprietary and could not be presented at this time. Nevertheless, the images and discussions are provided to demonstrate how the techniques can and have been used to examine in situ tissue distribution of therapeutic compounds.     

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.