From in vivo to in vitro/in silico ADME: progress and challenges

High-throughput screening technologies in biological sciences of large libraries of compounds obtained via combinatorial or parallel chemistry approaches, as well as the application of design rules for drug-likeness, have resulted in more hits to be evaluated with respect to their ADME or drug metabolism and pharmacokinetic properties. The traditional in vivo methods using preclinical species, such as rat, dog or monkey, are no longer sufficient to cope with this demand. This editorial discusses the changes towards medium- to high-throughput in vitro and in silico ADME screening. In addition, much more attention is now put on early safety and risk assessment of promising lead series and potential clinical candidates.     

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.     

Drug permeation in biomembranes: in vitro and in silico prediction and influence of physicochemical properties.

In the past decades, it has become increasingly apparent that in addition to therapeutic effect, drugs need to exhibit favourable absorption, distribution, metabolism and excretion (ADME) characteristics to produce a desirable response in vivo. As the recent progress in drug discovery technology enables rapid synthesis of vast numbers of potential drug candidates, robust methods are required for the effective screening of compounds synthesized within such programs, so that compounds with poor pharmacokinetic properties can be rejected at an early stage of drug development. Furthermore, a viable in silico method would save resources by enabling virtual screening of drug candidates already prior to synthesis. This review gives a general overview of the approaches aimed at predicting biological permeation, one of the cornerstones behind the ADME behaviour of drugs. The most important experimental and computational models are reviewed. Physicochemical factors underlying the permeation process are discussed.     

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.     

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.     

ADMET in silico modelling: towards prediction paradise?

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.     

4SCan/vADME: intelligent library screening as a shortcut from hits to lead compounds

Managing to solve the first step in drug discovery - the hit finding - can be a quite elaborate task, but it is only the initial step to the final goal; hit-to-lead optimisation still lies ahead and consumes even more time and resources. The solution is rather simple, that is, to take only the most promising compounds into account; but who is going to decide which ones are the most promising among a list of tens of millions of compounds in a virtual combinatorial library? 4SCan/vADME helps by bridging the gap between virtual (combinatorial) libraries designed by chemists and the in silico methods, docking and alignment, for screening databases. After choosing a random starting set, the implemented learning and prediction algorithm iteratively considers only combinations of fragments that have shown to result in more suitable interactions by the chosen method. ADME properties of the final list are then calculated via several in silico methods, resulting in a combined evaluation of the individual compound's target-specific, as well as ADME, properties. Based on the latter list of evaluated compounds, medicinal chemists can then decide which compounds might be the best ones to synthesise first and to serve as possible lead candidates. Following a brief introduction to virtual high-throughput screening techniques, the 4SCan/vADME method is outlined and discussed in this paper, using an example coming out of the 4SC pipeline.     

Computer systems for the prediction of xenobiotic metabolism

The aim of pharmaceutical research and development is to ensure a continuing pipeline of new chemical entities (NCEs) displaying high therapeutic efficacy with few or no side effects. Failure of promising lead candidates late in the drug discovery processes is regarded as commercially unacceptable in today's increasingly competitive business environment. An inappropriate ADME/Toxicity profile in humans is the major cause of failure of lead candidates in late clinical stages of drug development. Combinatorial chemistry techniques coupled with high throughput screening protocols means that pharmaceutical companies are now dealing with an unprecedented number of NCEs on an annual basis. As a consequence, screening for undesirable ADME/Toxicity properties in the early stages of drug development, preferably pre-synthesis, is now considered the essential paradigm. In silico assessment of NCEs is rapidly emerging as the next wave of technology for early ADME/Toxicity prediction. In this review, we discuss the major commercially available products for the assessing the potential metabolic activity of xenobiotic substances in mammalian systems.     

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.     

Which in vitro Screens Guide the Prediction of Oral Absorption and Volume of Distribution?

The development of medium to high-throughput in vitro screening of ADME (Absorption, Distribution, Metabolism, Excretion) properties has been the reply to higher demands on drug metabolism scientists to cope with progress in chemistry and biology. Two areas will be discussed here, namely screens for oral absorption and for volume of distribution. The prediction of these human pharmacokinetic parameters can be based on proper combination of simple physicochemical measurements. In the future in vitro screens most likely will be combined with in silico assessments of various ADME properties leading to the concept of in combo screening in drug discovery.     

Molecular field analysis (MFA) and other QSAR techniques in development of phosphatase inhibitors

Phosphatases are well known drug targets for diseases such as diabetes, obesity and other autoimmune diseases. Their role in cancer is due to unusual expression patterns in different types of cancer. However, there is strong evidence for selective targeting of phosphatases in cancer therapy. Several experimental and in silico techniques have been attempted for design of phosphatase inhibitors, with focus on diseases such as diabetes, inflammation and obesity. Their utility for cancer therapy is limited and needs to be explored vastly. Quantitative Structure Activity relationship (QSAR) is well established in silico ligand based drug design technique, used by medicinal chemists for prediction of ligand binding affinity and lead design. These techniques have shown promise for subsequent optimization of already existing lead compounds, with an aim of increased potency and pharmacological properties for a particular drug target. Furthermore, their utility in virtual screening and scaffold hopping is highlighted in recent years. This review focuses on the recent molecular field analysis (MFA) and QSAR techniques, directed for design and development of phosphatase inhibitors and their potential use in cancer therapy. In addition, this review also addresses issues concerning the binding orientation and binding conformation of ligands for alignment sensitive QSAR approaches.     

High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models

Evaluation and optimization of drug metabolism and pharmacokinetic data plays an important role in drug discovery and development and several reliable in vitro ADME models are available. Recently higher throughput in vitro ADME screening facilities have been established in order to be able to evaluate an appreciable fraction of synthesized compounds. The ADME screening process can be dissected in five distinct steps: (1) plate management of compounds in need of in vitro ADME data, (2) optimization of the MS/MS method for the compounds, (3) in vitro ADME experiments and sample clean up, (4) collection and reduction of the raw LC-MS/MS data and (5) archival of the processed ADME data. All steps will be described in detail and the value of the data on drug discovery projects will be discussed as well. Finally, in vitro ADME screening can generate large quantities of data obtained under identical conditions to allow building of reliable in silico models.     

Drug-like properties and the causes of poor solubility and poor permeability

There are currently about 10000 drug-like compounds. These are sparsely, rather than uniformly, distributed through chemistry space. True diversity does not exist in experimental combinatorial chemistry screening libraries. Absorption, distribution, metabolism, and excretion (ADME) and chemical reactivity-related toxicity is low, while biological receptor activity is higher dimensional in chemistry space, and this is partly explainable by evolutionary pressures on ADME to deal with endobiotics and exobiotics. ADME is hard to predict for large data sets because current ADME experimental screens are multi-mechanisms, and predictions get worse as more data accumulates. Currently, screening for biological receptor activity precedes or is concurrent with screening for properties related to "drugability." In the future, "drugability" screening may precede biological receptor activity screening. The level of permeability or solubility needed for oral absorption is related to potency. The relative importance of poor solubility and poor permeability towards the problem of poor oral absorption depends on the research approach used for lead generation. A "rational drug design" approach as exemplified by Merck advanced clinical candidates leads to time-dependent higher molecular weight, higher H-bonding properties, unchanged lipophilicity, and, hence, poorer permeability. A high throughput screening (HTS)-based approach as exemplified by unpublished data on Pfizer (Groton, CT) early candidates leads to higher molecular weight, unchanged H-bonding properties, higher lipophilicity, and, hence, poorer aqueous solubility.