ADME, and toxicology

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]. 

In Silico Surrogates for In Vivo Properties Profiling for ADME and Toxicological Behavior... 

As shown in this review, the complexity of pharmacophores can range from very simple objects (two- or three-point pharmacophores) to more sophisticated objects by the addition of more pharmacophoric features, different types of geometric constraints, shape, or excluded regions information. 2D (substructure) as well as ID (relational data) information can also be added to a 3D pharmacophore. The nature of the pharmacophoric points (feature vs. substructure) will directly affect the overall performance of a database search. In general, an overspecification of the pharmacophoric points will result in hit lists with limited structural diversity. However, the use of pharmacophores is an efficient procedure since it eliminates quickly molecules that do not possess the required features. Unfortunately, all the retrieved hits are not always active as expected since the presence of the pharmacophoric groups is only one of the multiple components that account for the activity of a molecule. Other properties (physicochemical, ADME, and toxicological properties) are other components of the multidimensional approach that is used to turn a hit into a drug. 

The present trends in high-throughput screening focus on reducing the size of libraries, on making them more effective and on excluding as early as possible ADME-and toxicological-inadequate candidates. 

Measured physicochemical properties (e.g., solubihty, p T, log P, mass spectroscopy, NMR) and co-crystal x-ray structures In vitro screening and secondary assay results In vivo prechnical animal ADME and toxicology data In sihco predictions for physicochemical properties, lead/drug models, ADME/ toxicology models, and activity models... 

Nonetheless, it is now generally accepted that it is worthwhile front-loading projects with ADME/PK and toxicology information in order to improve the chances of compounds achieving registration and becoming best in class [4]. 

Important progress in terms of higher throughput in ADME/PK work was realized recently by wider use of liquid chromatography/mass spectrometry (LC/MS), which has now become a standard analytical tool [26]. Flow NMR spectroscopy has become a routine method to resolve and identify mixtures of compounds and has found applications in drug metabolism and toxicology studies [27]. 

DeWitte, R.S. and Robins, R.H. (2006) ADME/Tox screening process. Expert Opinion on Drug Metabolism and Toxicology, 2 (5), 805-817. 

Lead optimisation is the synthetic modification of a biologically active compound, to fulfill all stereoelectronic, physicochemical, pharmacokinetic and toxicologic required for clinical usefulness (IUPAC). This phase begins with the first chemical lead or lead series selected for optimisation (i.e. the "lead series selected" milestone) and concludes with a decision for an optimized compound to enter preclinical development (i.e. the "pre-clinical candidate selected" milestone). This phase consists of testing of a compound to determine the chemical structure that has the optimum potency and selectivity for the target in question. The phase includes the search for backup compounds and may also include early ADME and toxicity evaluation. 

During preclinical development, the structure, physical and chemical characteristics, and stereochemical identity of the IND/CTA candidate are fully characterized. This information, for example, is required for the chemical manufacture and control (CMC) section of the IND. Appropriate bioanalytical methods are developed for the evaluation of pharmacokinetics, typically a series of studies focusing on absorption, distribution, metabolism, and excretion (ADME) in toxicology species, as well as systemic exposure and metabolism in toxicological and clinical studies. 

A more effective interpretation of pharmacological and toxicological data may usually be made if the ADMEs of the drug and its metabolites are well defined (see section 8.4). Tissue distribution data is usually obtained using single dose studies but repeated dose studies should be undertaken when ... 

The development of predictive models for drug-likeness, frequent hitters, ADME processes, and toxicological endpoints has so far yielded a great deal of soft filters (see discussion above and the compilation of ADMET computational models by Yu and Adedoyin [66]), and the trend still continues to improve both accuracy and... 

Clinical Pharmacokinetics (PK) and Toxicological (Tox) Datasets 255 Tablell.l Experimental ADME/Tox data for model development. 

Research on aromatic hydroxylation by cytochrome P450 provides an example of how quantum chemical calculations on small models can help in developing structure-reactivity relationships. Hydroxylation of C-H bonds is a particularly important class of reaction in drug metabolism,185 which can activate pro-drugs, or affect the bioavailability of pharmaceuticals. For the reliable prediction of pharmaceutical metabolism and toxicology (ADME/ TOX) properties, a key aim is the development of structure-activity relationships to predict conversions of drugs. Earlier work has shown that structure-activity relationships based on the structures and properties of substrates alone are of limited utility. There is a need for more detailed models, which can include effects of the reaction mechanism and specificity of different cytochrome P450 isozymes. 

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