Current Computer - Aided Drug Design - Volume 5, Issue 2, 2009
Volume 5, Issue 2, 2009
-
-
Edtiorial [Hot Topic: In Silico ADME/Tox Models: Progress and Challenges (Guest Editors: Robert K. DeLisle and David J. Diller)]
Authors: Robert K. DeLisle and David J. DillerOnce sufficient efficacy is obtained, the success or failure of a potential drug depends on its absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Since a tremendous amount of effort, for example high throughput screening and synthesis, has gone into finding bioactive molecules, ADMET issues are often the rate limiting factors in drug discovery programs. In response a significant amount of effort has gone into developing in silico models for a large variety of ADMET issues. The progress in several of these areas, blood-brain barrier penetration (Klon), protein binding (Hall), hERG inhibition (Diller), and hepatotoxicity (Cheng), are reviewed in this special issue. In addition, the limitations and best uses of in silico ADMET models are discussed in two additional reviews (Franklin, DeLisle). One particular challenge, mentioned in each of the reviews, facing anyone wishing to develop an in silico ADMET model is obtaining a large amount of consistent and appropriate data over a wide range of chemical structures. First, nearly all biological end points are the combination of multiple phenomena. Blood brain barrier penetration can occur by passive diffusion or active transport (Klon). In addition, it can be limited by being actively exported. Further, simple penetration into the central nervous system might not be sufficient to obtain efficacy. Ultimately, the free fraction in the brain will determine whether sufficient compound is available to achieve the desired efficacy. On the surface, binding to serum proteins or blockage of the hERG potassium channel are seemingly single phenomenological endpoints, but even these endpoints are more than simply binding to their respective proteins. Plasma protein binding involves multiple proteins and multiple binding sites within each protein (Hall). In addition, the kinetics of binding can be quite important whereas equilibrium binding is usually just considered. To bind to the hERG channel pore, the likely site of binding for most hERG blockers, a molecule must first pass through the cell membrane and then enter the channel through the activation gate (Diller), thus, cell permeability is an important piece to understanding hERG blockers. Of all the endpoints, hepatotoxicity is clearly the most complex and challenging. The hepatotoxic potential of a compound likely arises through many different mechanisms, is highly dependent on compound concentration, and is highly dependent on metabolites and intermediates thereof. Of course, metabolism itself can occur through many pathways making predicting the hepatoxic potential of a compound that much more difficult. Even if a large data set were available from a single source, one still must ask the question whether any one in vitro assay format for a particular end point is clearly superior to another. Assays invariably change over time because they are not perfect predictors of in vivo or clinical results and are tuned as more information becomes available. Thus a model fit too heavily to data from a particular assay might win the battle - better predictions for that assay - but lose the war - poorer predictions for in vivo or clinical effects. Clearly the ultimate goal of in silico models is to provide guidance on the in vivo effects (or more specifically, in human effects) of assessed compounds, so one must choose in vitro endpoints carefully keeping in mind their relationship to the in vivo goal.
-
-
-
Computational Models for Central Nervous System Penetration
More LessAs the population of the elderly increases, there is a growing need for drugs to treat a variety of neurological diseases, such as Alzheimer's disease, Parkinson's disease, brain cancer, stroke, and infections in the central nervous system (CNS). Conversely, there is a need to identify brain penetration, and therefore potentially adverse events, from drugs acting on non-CNS targets. Late-stage clinical failures are costly in the drug discovery process, giving rise to the need for models to predict blood-brain barrier (BBB) penetration. In vivo and ex vivo models are expensive, time-consuming, and labor-intensive, giving rise to the development of in vitro and in silico models to aid in drug development early in the discovery process. Recent years have seen an increased emphasis on predictive computational models of CNS penetration. We review the progress in computational models of CNS penetration over the last five years. Computational models reported in the literature usually model the ratio of brain to blood levels for a molecule. These models can be broken down into logBB models, which attempt to predict a discrete value for the logarithm of the brain:blood ratio, and binary classifiers, which classify molecules as either brain-penetrant or non-penetrant according to an arbitrary cutoff. We also discuss whether the brain:blood ratio is an appropriate metric to use in predicting CNS penetration and the need for alternative endpoints that measure the information medicinal chemistry teams are actually interested in, such as the permeabilitysurface (PS) product and the fraction unbound (fu) in the brain.
-
-
-
Methods for Predicting the Affinity of Drugs and Drug-Like Compounds for Human Plasma Proteins: A Review
Authors: L. M. Hall, Lowell H. Hall and Lemont B. KierSignificant research has been conducted in the area of developing in silico methods for predicting the affinity of drugs and drug-like compounds for human plasma proteins. The free fraction of a compound associated with a given level of binding affinity has a significant impact on the pharmacokinetic profile of a drug and its metabolites. The development of quality plasma protein binding models has become an important goal in assisting the drug optimization process. The structure, binding sites and binding interaction modes of common plasma proteins are discussed along with the protein composition of human plasma and pharmacokinetic consequences of protein binding profiles. A short section outlines current methods for measuring binding affinity for plasma proteins. A total of eighteen published studies were reviewed for this article and the statistical results from 42 models are tabulated and compared. Models are compared on the basis of the endpoint modeled, method of structure description, learning algorithm, validation criteria, and statistical results. The role of logP as an input descriptor and the possible utility of reported models in categorizing virtual compounds are also discussed.
-
-
-
In Silico hERG Modeling: Challenges and Progress
More LessThe first generation of in silico hERG models have focused on key driving forces for hERG/ligand binding: basicity and lipophilicity. While reducing basicity and lipophilicity are typically the first line approaches to eliminating hERG blocking activity, in many programs these are not feasible approaches because basicity and lipophilicity are frequently necessary for achieving good pharmacokinetics or primary target binding. Thus for the second, generation of in silico hERG models, a focus on understanding the more subtle aspects of hERG binding is necessary. Thus, the focus of this review is on extracting ideas, either qualitative or quantitative, from the various modeling efforts as to how hERG activity might be eliminated or diminished beyond simply reducing basicity and lipophilicity. In addition, several areas where key questions around hERG binding have not been adequately addressed by in silico work are highlighted.
-
-
-
In Silico Prediction of Hepatotoxicity
By Ailan ChengLiver toxicity has been one of the major causes for drug attrition. It has caused discontinuation of preclinical and clinical studies, and withdrawals and black box warnings of marketed drugs. Drug-induced hepatotoxicity may lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important issues in the drug development cycle. In silico models based on chemical structure provide a cost effective approach for hepatotoxicity, and it can help prioritize lab tests, preclinical and clinical studies. There is no doubt as to the need for predictive methods in every stage of the drug discovery pipeline. However there is a serious shortage of consistent data for the development of reliable models and developing genuinely predictive models for hepatotoxicity remains a challenge.
-
-
-
In Silico Studies in ADME/Tox: Caveat Emptor
More LessThere is great appeal, scientifically, financially and temporally, for the use of predictions in the drug metabolism and toxicology evaluations used, and in many instances, required, in chemical compound design and development. Indeed, there have been great strides in hardware and software development, as well as many scientific advances that have made predictions not only feasible but in many instances, accurate, when compared with data acquired from lengthy and costly human or animal studies. However, despite the wide array of available software capable of making predictions in the fields of ADME/Tox, the user needs to err on the side of caution when attempting to fit their data from a new structural entity into a database where the learning set may be somewhat or even grossly different from the new entity. There must be a great deal of thought given to what programs are to be utilized as well as to the robustness of the data being entered - there is still no replacement for the expression “garbage in, garbage out”. Further, decisions must be made as to whether the predictions made by the software are superior to other ways of gaining human predictions from in vitro and in vivo ADME/Tox information - if acquiring in vitro data is easier (high throughput) and more reliable, then maybe a new predictive software program is not the best answer, but it if is, we need to choose wisely. Acceptance of predictive software is difficult but it will, eventually, be well worth the effort. Until that time, the end-user needs to go into the prediction business with his or her eyes wide open as to the potential pitfalls and ramifications of an over exuberance of predictions. “However beautiful the strategy, you should occasionally look at the results”. Winston Churchill (1874-1965). British Statesman.
-
-
-
Beyond the Model
More LessIt is not unusual for models developed to predict Absorption, Distribution, Metabolism, Elimination, and Toxicity (ADMET) as well as other endpoints to be nothing more than a black box, supplying only a numerical or categorical value by which users are expected to assess the goodness of a query compound. This type of result is, however, of little use in the overarching goal of developing better and safer drug compounds, that is, providing guidance toward improving the characteristics of the query or formulating chemical hypotheses that can be evaluated through synthetic efforts. As a result, the level of acceptance of predictions by users outside the computational chemistry and molecular modeling groups tends to be very low. In this review, I address three primary domains in which model presentation can be improved, specifically, establishing confidence in the prediction, data interrogation, and model interpretability. It has been my experience that even small efforts in these areas result in a much greater return with respect to acceptance, good faith, and usage from users regardless of the user's role within the drug discovery process.
-
Volumes & issues
-
Volume 21 (2025)
-
Volume 20 (2024)
-
Volume 19 (2023)
-
Volume 18 (2022)
-
Volume 17 (2021)
-
Volume 16 (2020)
-
Volume 15 (2019)
-
Volume 14 (2018)
-
Volume 13 (2017)
-
Volume 12 (2016)
-
Volume 11 (2015)
-
Volume 10 (2014)
-
Volume 9 (2013)
-
Volume 8 (2012)
-
Volume 7 (2011)
-
Volume 6 (2010)
-
Volume 5 (2009)
-
Volume 4 (2008)
-
Volume 3 (2007)
-
Volume 2 (2006)
-
Volume 1 (2005)
Most Read This Month
