In Silico Approaches

There are a variety of plate-based electrophysiology systems available to test for effects of drugs on cardiac ion channels overexpressed in ceU lines. Conductance block data (i.e., the molar concentration of a drug producing 50% inhibition of an ionic current (IC )) that are derived from these systems have enabled assessment of the key molecules responsible for the cardiac AP as a means of early cardiac safety assessment (Pollard et al., 2010). The questions therefore become (i) What can be done to integrate these multiple experimental IC measurements (ii) How can ICj  

Uncertainty enters the equation with respect to either a drug s effect (IC ) or variability between experimental platforms, laboratories, cell types, and intra-animal/intraspe-eies differences. In a paper by Elkins et al. (2013), analysis of the typical variance in experimentally measured IC values of multiple ion channels at different laboratories allowed to determine when repeated ion channel screens should be performed to reduce uncertainty in a drug s action to acceptable levels to allow a meaningful interpretation of the data. While determination of the experimental uncertainty can be quantified, the uncertainty based on different 

The logical progression is in considering how best to improve a model s performance by considering what additional data to include. A study by Di Veroli et al. (2013) considers the kinetics of drug s block that can influence use and voltage dependence and how this may lead to the development of more predictive in silico models. It is 

FIGURE 93 Predicted impact and balance of varying three key ionic currents on the duration of the cardiac action potential at 90% repolarization (APD90). (See insert for color representation of the figure.) 

4 FROM ANIMAL EX VIVO/IN VITRO MODELS TO HUMAN STEM CELL-DERIVED CMs FOR CARDIAC SAFETY TESTING 

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Ekins S, Wrighton SA. Application of in silico approaches to predicting drng-drug interactions a commentary. J Pharm Tox Methods 2001 44 1-5. 

Because of its convenience and good patient compliance, oral administration is the most preferred drug delivery form. As a result, much of the attention of in silico approaches is focused on modeling drug oral absorption, which mainly occurs in the human intestine. In general, drug bioavailability and... 

The use of computational techniques to predict toxicity, or in silico approaches, aims to decrease costs and reduce animal suffering for chemical risk assessment. 

To verify the prediction from in silico approach, a BALB/c 3T3 CTA was performed on PFOA and PFNA. Results from in vitro assay confirm the response from QSARs models. 

The volume is divided into five sections. Part one looks at the experimental study of membrane permeability and oral absorption. In Part two, problems of measuring and prediction solubility, as one of the key determinants in the absorption process, will be discussed in detail. In the next part, progress in the science around transporter proteins and gut wall metabolism and their effect on the overall absorption process is presented. Part four looks at the in silico approaches and models to predict permeability, absorption and bioavailability. In the last part of the book, a number of drug development issues will be highlighted, which could have an important impact of the overall delivery strategies for oral pharmaceutical products. 

The key feature of the PDA technology is that a far wider biological diversity can be screened computationally than it is possible with exclusively experimental methods. The in silico approach provides unique and original gene modifications whilst maintaining the precise control over the extent and nature of protein modifications. The novel sequences... 

Prediction of Human Volume of Distribution Using in vivo, in vitro, and in silico Approaches... 

If the assertion that VD is driven by non-specific interactions between drugs and macromolecular structures in tissues, then it logically follows that VD would be correlated to physiochemical parameters. Since such parameters are amenable to computation from structure alone, the prediction of human VD from chemical structure is feasible. Such in silico approaches have only been described over the past few years, as computational chemistry tools have advanced. 

In an early application of in silico approaches to predict human VD, Ritschel and coworkers described an approach using artificial neural networks (ANN), in this case for VDp [34]. However, this was not a truly in silico-only approach as the ANN that yielded accurate predictions of human VD required animal pharmacokinetic data as input parameters, along with in vitro data (protein binding and logP). 

Ekins, S. (2003) In silico approaches to predicting drug metabolism, toxicology and beyond. Biochemical Society Transactions, 31, 611-614. 

Elizabeth Barrett Browning after hearing a discussion of toxicity prediction, How can I kill thee, let me count the ways . A recent article by Stouch et al. [74] presents a thoughtful analysis of the validation effort for four such ADME/Tox models. Oprea et al. [75, 76[ have compared drugs leads with compounds in development and in the marketplace and shown that compounds increase in molecular weight and logP as they progress to the bedside. In silico approaches certainly have their place in the pharmaceutical industry as one more tool to increase the probability of success [77]. 

A recent review evaluating virtual screening lists in terms of predicted ADME and toxicology properties demonstrates some advantages of the integration of in silico approaches in search for viable lead structures [298]. 

The AChE inhibitory properties of the coumarins scopoletin (75) and scopolin (76) were discovered by an interesting in silico approach. A model 3-dimensional pharmacophore was constructed, using a database of known inhibitors and their interaction with the AChE from Torpedo californica. The model was then used to predict likely inhibitors from a large database of those whose molecular coordinates were known. (75) and (76) were predicted and then isolated from Scopolia carniolica and tested in the Ellman assay. Results showed that (75) was much more active than the glucoside (76) but it was 2.5 orders of magnitude weaker than galantamine. Scopoletin also showed activity in vivo when given to rats. 

Usually, PAMPA does not have any aqueous pores and is therefore not suitable for examining paracellular transport. Some cell models, for example, Caco-2 and MDCK, have a narrower tight junction than the in vivo human intestine and may underestimate paracellular transport. However, the contribution of the paracellular pathway can be added using an in silico approach [76-78]. 

In summary, in silico approaches to aid in removing D DI should be actively applied in early lead development. The application of these models in early drug discovery will reduce the frequency and cost of drug candidates that require clinical DDI studies. In the future, it is possible that a great majority of CYP-based DDI issues will be removed during lead development. 

Obach, R.S. (2007) Prediction of human volume of distribution using in vitro, in vivo and in silico approaches. Annual Reports in Medicinal Chemistry, 42, 469-488. 

ECVAM is the leading international center for alternative test method validation. Hartung et al. (29) summarized the modular steps necessary to accomplish stage 3 (test validation). The seven modular steps are (I) test definition, (2) within-laboratory variability, (3) transferability, (4) between-laboratory variability, (5) predictive capacity, (6) applicability domain, and (7) performance standards (29). Steps 2-4 evaluate the test s reliability steps 5 and 6 evaluate the relevance of the test. Successful completion of all seven steps is necessary to proceed to stage 4 (independent assessment or peer review). This modular approach allows flexibility for the validation process where information on the test method can be gathered either prospectively or retrospectively. The approach is applicable not only to in vitro test methods but also to in silico approaches (e.g., computer-based approaches such as quantitative structure-activity relationships or QSAR) and pattern-based systems (e.g., genomics and proteomics). 

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