ADMET Modeler

Votano JR (2005) Recent uses of topological indices in the development of in silico ADMET models. Curr. Opin. Drug Dicovery Dev. 8 32-37. 

II. Product Summaries Simulations Plus develops simulation and predictive modeling software for in silico compound screening and for preclinical and clinical drug development in the area of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET). The available applications include GastroPlus, ADMET Predictor, ADMET Modeler, DDDPlus, and MembranePlus. 

Segall M, Champness E, Obrezanova O, Leeding C (2009) Beyond profiling using ADMET models to guide decisions. Chem Biodivers 6(11) 2144-2151... 

Functionalized Polyethylene via ADMET Model Copolymers of Ethylene and Vinyl Monomers... 

More industrial polyethylene copolymers were modeled using the same method of ADMET polymerization followed by hydrogenation using catalyst residue. Copolymers of ethylene-styrene, ethylene-vinyl chloride, and ethylene-acrylate were prepared to examine the effect of incorporation of available vinyl monomer feed stocks into polyethylene [81]. Previously prepared ADMET model copolymers include ethylene-co-carbon monoxide, ethylene-co-carbon dioxide, and ethylene-co-vinyl alcohol [82,83]. In most cases,these copolymers are unattainable by traditional chain polymerization chemistry, but a recent report has revealed a highly active Ni catalyst that can successfully copolymerize ethylene with some functionalized monomers [84]. Although catalyst advances are proving more and more useful in novel polymer synthesis, poor structure control and reactivity ratio considerations are still problematic in chain polymerization chemistry. 

A family of ADMET model copolymers were synthesized and used to study the effects of regular microstructure on polymer properties, in particular functionahzed polyethylenes. The structure-property relationships of various ethylene copolymers can be clarified using these model systems. This is illustrated in Fig. 3 by the relationship of to functional group size. Future studies on these and similar systems should lead to fundamental discoveries concerning the class of materials known as polyethylenes and their physical properties. 

We also discussed factors and models that need to be employed for the design against specific biological activities, and then introduced the importance of ADMET models. We have also reviewed ways to conduct comprehensive, balanced, multi-objective compound library design. These multi-objective methods should be applicable to gene family oriented library design as well, so... 

Li AP. Building predictive ADMET models for early decisions in drug discovery. Drug Discov Today. 2001 6 357-366. 

This chapter describes some of the approaches and techniques used currently to derive in silico models for the prediction of absorption, distribution, metabolism, elimination/excretion, and toxicity (ADMET) properties. The chapter also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intention of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development. 

One consideration to take into account in ADMET modeling is the availability of relevant and accurate datasets. In general, there exists a relatively small... 

Although a majority of the published ADMET models are based on linear multivariate methods as discussed in Section 16.3.3.1, other nonlinear methods have also been employed. The most commonly used nonlinear method in ADMET modeling is neural networks (NNs). Backpropagation NNs have been used to model absorption, permeation, as well as solubility and toxicological effects. A particular problem for many NNs is the tendency for these networks to overtrain (see further discussions on model validation in Section 16.3.3.4), which needs to be closely monitored to avoid the situation where the derived model becomes an encyclopedia , that is, the model can perfectly explain the variance of the investigated property of the compounds used to derive the model but have quite poor predictive ability with respect to new compounds. 

It is rather essential that the applicability domain of a derived model can be evaluated so that outliers to the model may be indicated. If an established statistical model is to be regarded as poor from a predictive point of view this should be done on the basis of correct reasons, that is, that the model has truly poor predictive ability and not from the fact that the model cannot estimate outliers to the model with acceptable accuracy. The latter case is probably the most common cause for statistical (ADMET) models to fall from fame especially those that can be accessed through internal or external web services. In many cases it is difficult, if not impossible, to find out about the compounds used as training set and/or the chemical description used in the model. Thus, many compounds outside the applicability domain of the model will be submitted. It is therefore of great importance to have an indication together with the prediction whether the compound is considered to fall inside or outside of this domain, that is, if the compound is an outlier or not. The outlier information, and possibly also how far from the model the compound in question is, may in many cases be utilized in a more proactive way than just realizing that a number of compounds submitted to the model for prediction are, in fact, outliers to the present model. Thus, by analyzing the outliers, perhaps virtual compounds, from various points of views, for example, structural or synthetic, some of these compounds may later be synthesized and tested experimentally. The same compounds may then be incorporated into a revised model that will have a broader applicability domain. There are different methods available to determine whether a particular compound is to be labeled as an outlier. In this section, we will describe two of these methods ... 

The quality of a model may change with time and different model learners may show different abilities to extrapolate forward with time. As David Clarke recently highlighted, There is a growing awareness that the validation of ADMET models should not be a one-off process and that models need to be revalidated periodically to make sure their predictive power is not waning as new chemical classes emerge for prediction... 

Local ADMET models are derived from molecules belonging to one specific che-motype and are only apphcable for the prediction of compounds belonging to the same scaffold. They are frequently used in lead optimization and depend on the availabihty of a balanced set of molecules with data reflecting a single mechanism of action on the target or ADMET property of interest. The number of compounds used to build local models is therefore typically much smaller than for global models. 

This chapter briefly describes stracture- and ligand-based ADMET modeling and then focuses in detail on building a global model for metabolic stability of compounds including a recent application in a research project. 

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