Silica modeling

Norinder U. In silica modelling of ADMET—a minireview of work from 2000 to 2004. SAR QSAR Environ Res 2005 16 1-11. 

Figure 20.1 In silica modeling targets of drug disposition.

The study of DMPK has changed from a descriptive to a much more predictive science [3]. This is driven by great progress in bioanalytics, development of in vitro assays and in silica modeling/simulation, and a much better basic understanding of the processes. Thus, and fortunately, ADME-related attrition has lowered from around 40% in 1990 to around 10% in 2005 [13]. 

The experimental Pjpp data have been used to build predictive models. However, since PAMPA is already a model, an in silica model based on this is a model of a model. The predictability for in vivo permeability or absorption of such in silica PAMPA model can be queshoned (see Eq. 11), since it is two steps from reality ... 

Van de Waterbeemd, H., Gifford, E. ADMET in silica modeling towards in... 

Aronov, A. M. Predictive in silica modeling for hERG channel blockers. Drug Discov. Today 2005, 10,149-155. 

Galland, A. Towards the validation of in silica models and physicochemical filters to identify and characterize new chemical entities, PhD Thesis, University of Lausanne, 2004. 

Figure 14 Self-diffusivity D versus configurational entropy Sc data (symbols) for the BKS silica model at two different density p values. The lines are fits to the AG form given by Eq. [10] with tr

Pharmacokinetics and toxicity have been identified as important causes of costly late-stage failures in drug development. Hence, physicochemical as well as ADMET properties need to be fine-tuned even in the lead optimization phase. Recently developed in silica approaches will further increase model predictivity in this area to improve compound design and to focus on the most promising compounds only. A recent overview on ADME in silica models is given in Ref [128]. 

Several in silica models for prediction of oral absorption are available [133-136]. Simple models are based on only few descriptors like logP, logD, or polar surface area (PSA), while they are only applicable if the compounds are passively absorbed. In case of absorption via active transporters or if efflux is involved, prediction of absorption is still not successful. 

The approach discussed to use VolSurf derived in silica models to understand structure-PK relationships for pharmacokinetic properties was also applied to one series of selective cardiac KATP channel blockers [160]. It was found that compounds fulfilling the predefined selectivity profile exhibit only less-optimal pharmacokinetic properties because of a short plasma mean residential time (MRT). Consequently, the MRT for 28 compounds from rabbit iv studies for one series was used as dependent variable to derive a VolSurf PLS model in addition to ligand affinity SAR data. The chemical... 

Classically the term in silica modeling referred to the process of building a model to predict a given endpoint from a set of molecular properties derived purely from the chemical structure of a number of compounds of which the endpoint of interest is known. 

Unfortunately the number of in silica modeling studies on brain membrane permeability is significantly smaller than for human intestinal absorption, resulting in a lack of consensus about how to assess brain penetration (both in vitro and in vivo) and the intrinsic difficulty of measuring this particular endpoint, which overall results in a low turnover of data generation that could be used to build in silica models [95, 96]. For this reason, the in silica models used to assess brain permeability in the discovery process focus normally on P-gp efflux and some measure of in vitro membrane permeability, methods which are reviewed in the next section. 

One of the main in vitro permeability assays used in the pharmaceutical industry has been for many years the Caco-2 monolayer. Therefore, most of the in silica models developed to predict permeability were based on Caco-2 data. Hou and Johnson produced a couple of reviews that comprehensibly summarizes the recent efforts using Caco-2 permeability data [92, 94]. All those models are designed to predict the influx or apparent permeability of drugs in the same direction as intestinal absorption occurs, that is, from the apical to the basal side of the cell line, regardless of the extent of active transport involved in the permeation process. 

As the current trends show, the in silica modeling of membrane permeability is evolving towards a better integration of the contemporary understanding of absorption pathways in the predictive systems [112]. If the hit-to-lead in silica profiling process wants to succeed in the prediction of in viva absorption, in silica modeling needs to expand in the following three directions ... 

And finally, whilst this chapter focuses on CYP enzymes, the same principles can be applied to any enzyme or transporter providing the tools to do the job are available (e.g., in silica models, specific substrates/inhibitors, recombinant systems). 

Now we can consider the pressure gap and the structure gap. Concerning the pressure gap, it was concluded from IR spectroscopy that between UHY up to 10 Torr the reaction mechanism was the same for Pd/silica model catalyst [125]. 

When building in silica models for biopharmaceutical properties, one must understand the molecular nature of the property in order to select molecular descriptors that have relevance to the problem. Plasma protein binding involves the reversible... 

Fig. 9 shows the Si MAS-NMR spectra of a silica model substrate and two different sandstones after silylation with methyltriethoxysilane. 

De Vos Burchart et al. have recently developed a force field for modeling zeolites.21 The model originally was intended for all-silica zeolites but was quickly extended to aluminum-containing zeolites. The parameters were derived from several sources. Standard bond dissociation energies were used, and the force constants were refined to fit the structure of ZSM-5, the structure and frequencies of a-quartz, in addition to unit cell dimensions of other zeolites. With the all-silica model, the authors were able to calculate heats of formation... 

As shown in Figure 4 a very narrow pore size distribution characterises ERS-8 with respect to SA, evaluated with DFT (silica model). The morphological parameters are collected in Table 2. 

MCM-41 and HMS materials show adsorption at a pressure lower than the threshold at 0.43 p/p°. In this region it is difficult to evaluate the pore size with classical method based on the Kelvin equation, because both micropore filling and capillary condensation can occur. Instead DFT (silica model) permits a better evaluation of pore size distribution in this region, observing a very narrow pore size distribution for lVICM-41 (Figure 6, curve b)... 

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