In silico computational models

A recent EDA white paper entitled Challenge and Opportunity on the Critical Path to New Medical Products (PDA, March 2004) states that We need to make the effort required to create better tools for developing medical technologies , and ...there is hope that greater predictive power may be obtained from in silico (computer modeling) analyses. ... Some believe that extensive use of in silico technologies could reduce the overall cost of drug development by as much as 50 percent . 

The term virtual screening or in silico screening" is defined as the selection of compounds by evaluating their desirability in a computational model. The desirability comprises high potency, selectivity, appropriate pharmacokinetic properties, and favorable toxicology. 

In Silico ADME Modeling 3 Computational Models to Predict Human Intestinal Absorption Using Sphere Exclusion and kNN QSARMethods. QSAR Combinatorial Science, 5, 653-668. 

Gunturi, S.B., Narayanan, R. and Khandelwal, A. (2006) In silico ADME modelling 2 Computational models to predict human serum albumin binding affinity using ant colony systems. Bioorganic and Medicinal Chemistry, 14, 4118 129. 

Onchidal and fasciculins are interesting natural compounds and it is difficult to predict their toxicity. In the case of onchidal, in silico computational predictive modeling for toxic endpoints of interest may prove useful for risk assessment decision support. Likewise, it is a challenge to predict the mditary potential and human impact of these natural toxins since their affinity for enzyme inhibition depends upon the amount and duration of the human exposure. 

Gunturi, S.B. and Narayanan, R. (2007) In silico ADME modeling 3 computational models to predict human intestinal absorption using sphere exclusion... 

Maximal recommended therapeutic dose (MRTD as defined by the US FDA) has been correlated with potential toxicity. Matthews et al. have reported in silico multicase models for MRTD [55]. According to their analysis, more toxic molecules will have MRTD values less than 2.7 mg/kg/day and less toxic molecules will have MRTD more than 4.99 mg/kg/day. If one builds an in silico model for MRTD, then a new rule can be added to the computational alert AD ME rules described above. 

Compilation of descriptors, size of datasets, and statistical models used, and accuracy of published in silico absorption models. Several classification models can be found in the literature, which are regarded as qualitative models and therefore not reported. Caco-2 and FA data were selected for the compilation, since these are the main responses used in the development of computational models. However, other responses such as permeability in 2/4/A1 cell monolayers, artificial membranes, and the MDCK cell line, have also been used as responses in the computational model development. 

Due to the aforementioned discrepancy in data availability (especially relevant to translation of toxic effect) and the fact that many clinical endpoints are multi-mechanistic, it is important to stress that each computational step should be well defined and model small steps, for example, a traditional quantitative structure-activity relationship (QSAR) approach based on chemical structure is probably relevant to distinguish hERG binders from nonbinders, but not relevant to model a small set of diverse compounds associated with a complex endpoint such as drug induced liver injury (DILI). A second important factor to consider when construchng in silico safety models is the intended use of the model, and the potential cost associated with false positives versus false negatives from the model. For instance, there is zero... 

If structural information of the protein target is available, e.g., a crystal structure, in silico screening of huge virtual compound libraries can be conducted by the use of docking simulations. Based on identified primary hits, structural variations of the ligand can be evaluated by computational modeling of the ligand-protein complex. 

To date, many of the reported ADME/Tox models have been rule based. For example, some research groups have used relatively simple filters like the rule of 5 [93] and others [94] to limit the types of molecules evaluated with in silico methods and to focus libraries for HTS. However, being designed as rapid computational alert tools aimed at a single property of interest, they cannot offer a comprehensive picture when it comes to understanding ADME properties. 

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