Prediction of Fraction Unbound in Microsomal and Hepatocyte Incubations: A Comparison of Methods across Industry Datasets.

The fraction unbound in the incubation, fu,inc, is an important parameter to consider in the evaluation of intrinsic clearance measurements performed in vitro in hepatocytes or microsomes. Reliable estimates of fu,inc based on a compound's structure have the potential to positively impact the screening timelines in drug discovery. Previous works suggested that fu,inc is primarily driven by passive processes and can be described using physicochemical properties such as lipophilicity and the protonation state of the molecule. While models based on these principles proved predictive in relatively small datasets that included marketed drugs, their applicability domain has not been extensively explored. The work presented here from the in silico ADME discussion group (part of the International Consortium for Innovation through Quality in Pharmaceutical Development, the IQ consortium) describes the accuracy of these models in large proprietary datasets that include several thousand of compounds across chemical space. Overall, the models do well for compounds with low lipophilicity. In other words, the equations correctly predict that fu,inc is, in general, above 0.5 for compounds with a calculated logP of less than 3. When applied to lipophilic compounds, the models failed to produce quantitatively accurate predictions of fu,inc, with a high risk of underestimating binding properties. These models can, therefore, be used quantitatively for less lipophilic compounds. On the other hand, internal machine-learning models using a company's own proprietary dataset also predict compounds with higher lipophilicity reasonably well. Additionally, the data shown indicate that microsomal binding is, in general, a good proxy for hepatocyte binding.

In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development.

In silico tools to investigate absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK) properties of new chemical entities are an integral part of the current industrial drug discovery paradigm. While many companies are active in the field, scientists engaged in this area do not necessarily share the same background and have limited resources when seeking guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting. This work summarizes the views of a group of industrial in silico and experimental ADME scientists, participating in the In Silico ADME Working Group, a subgroup of the International Consortium for Innovation through Quality in Pharmaceutical Development (IQ) Drug Metabolism Leadership Group. This overview on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicinal chemists, computational chemists, and DMPK scientists working in drug design to increase their knowledge in the area.

G-protein alpha and beta-gamma subunits interact with conformationally distinct signaling states of rhodopsin.

Light activated rhodopsin interacts with domains on all three subunits of transducin. Two of these domains, the C-terminal regions of the alpha and gamma subunits mimic the ability of transducin to stabilize the active conformation of rhodopsin, metarhodopsin II, but display different roles in transducin activation process. Whether the interactions are with the same or different complimentary sites on Meta II is unknown. We have used chemo-selective thioalkylation of rhodopsin and UV/visible spectroscopy to show that interactions with transducin C-terminal domains can be selectively disrupted. These data provide evidence that formal structural determinants on Meta II for these domains of transducin are different. In a set of complimentary experiments we examined the reactivity of Meta II species produced in the presence of the Gtalpha and Gtgamma subunit peptides to hydroxylamine. Analysis of the rates of Meta II decay confirms that the conformational states of Meta II when bound to Gtalpha and Gtbetagamma represent distinct signaling states of rhodopsin.

Computational models for predicting interactions with cytochrome p450 enzyme.

Cytochrome p450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. As only 6 isoenzymes are responsible for approximately 90 % of known oxidative drug metabolism, a number of frequently prescribed drugs share the CYP-mediated metabolic pathways. Competing for a single enzyme by the co-administered therapeutic agents can substantially alter the plasma concentration and clearance of the agents. Furthermore, many drugs are known to inhibit certain p450 enzymes which they are not substrates for. Because some drug-drug interactions could cause serious adverse events leading to a costly failure of drug development, early detection of potential drug-drug interactions is highly desirable. The ultimate goal is to be able to predict the CYP specificity and the interactions for a novel compound from its chemical structure. Current computational modeling approaches, such as two-dimensional and three-dimensional quantitative structure-activity relationship (QSAR), pharmacophore mapping and machine learning methods have resulted in statistically valid predictions. Homology models have been often combined with 3D-QSAR models to impose additional steric restrictions and/or to identify the interaction site on the proteins. This article summarizes the available models, methods, and key findings for CYP1A2, 2A6, 2C9, 2D6 and 3A4 isoenzymes.

Relative strength of cation-pi vs salt-bridge interactions: the Gtalpha(340-350) peptide/rhodopsin system.

Interactions between cationic and aromatic side chains of amino acid residues, the so-called cation-pi interaction, are thought to contribute to the overall stability of the folded structure of peptides and proteins. The transferred NOE NMR structure of the G(t)alpha(340-350) peptide bound to photoactivated rhodopsin (R*) geometrically suggests a cation-pi interaction stabilizing the structure between the epsilon-amine of Lys341 and the aromatic ring of the C-terminal residue, Phe350. This interaction has been explored by varying substituents on the phenyl ring to alter the electron density of the aromatic ring of Phe350 and observing the impact on binding of the peptide to R*. The results suggest that while a cation-pi interaction geometrically exists in the G(t)alpha(340-350) peptide when bound to R*, its energetic contribution to the stability of the receptor-bound structure is relatively insignificant, as it was not observed experimentally. The presence of an adjacent and competing salt-bridge interaction between the epsilon-amine of Lys341 and the C-terminal carboxylate of Phe350 effectively shields the charge of the ammonium group. Experimental data supporting a significant cation-pi interaction can be regained through a series of Phe350 analogues where the C-terminal carboxyl has been converted to the neutral carboxamide, thus eliminating the shielding salt-bridge. TrNOE NMR experiments confirmed the existence of the cation-pi interaction in the carboxamide analogues. Various literature estimates of the strength of cation-pi interactions, including some that estimate strengths in excess of salt-bridges, are compromised by omission of the relevant anion in the calculations.

Development of CYP3A4 inhibition models: comparisons of machine-learning techniques and molecular descriptors.

Computational models of cytochrome P450 3A4 inhibition were developed based on high-throughput screening data for 4470 proprietary compounds. Multiple models differentiating inhibitors (IC(50) <3 microM) and noninhibitors were generated using various machine-learning algorithms (recursive partitioning [RP], Bayesian classifier, logistic regression, k-nearest-neighbor, and support vector machine [SVM]) with structural fingerprints and topological indices. Nineteen models were evaluated by internal 10-fold cross-validation and also by an independent test set. Three most predictive models, Barnard Chemical Information (BCI)-fingerprint/SVM, MDL-keyset/SVM, and topological indices/RP, correctly classified 249, 248, and 236 compounds of 291 noninhibitors and 135, 137, and 147 compounds of 179 inhibitors in the validation set. Their overall accuracies were 82%, 82%, and 81%, respectively. Investigating applicability of the BCI/SVM model found a strong correlation between the predictive performance and the structural similarity to the training set. Using Tanimoto similarity index as a confidence measurement for the predictions, the limitation of the extrapolation was 0.7 in the case of the BCI/SVM model. Taking consensus of the 3 best models yielded a further improvement in predictive capability, kappa = 0.65 and accuracy = 83%. The consensus model could also be tuned to minimize either false positives or false negatives depending on the emphasis of the screening.

Prediction of drug-like molecular properties: modeling cytochrome p450 interactions.

Preventing drug-drug interactions and reducing drug-related mortalities dictate cleaner and costlier medicines. The cost to bring a new drug to market has increased dramatically over the last 10 years, with post-discovery activities (preclinical and clinical) costs representing the majority of the spend. With the ever-increasing scrutiny that new drug candidates undergo in the post-discovery assessment phases, there is increasing pressure on discovery to deliver higher-quality drug candidates. Given that compound attrition in the early clinical stages can often be attributed to metabolic liabilities, it has been of great interest lately to implement predictive measures of metabolic stability/ liability in the drug design stage of discovery. The solution to this issue is wrapped in understanding the basic of the cytochrome P450 (CYP) enzymes functions and structures. Recently, experimental information on the structure of a variety of cytochrome P450 enzymes, major contributors to phase I metabolism, has become readily available. This, coupled with the availability of experimental information on substrate specificities, has lead to the development of numerous computational models (macromolecular, pharmacophore, and structure-activity) for the rationalization and prediction of CYP liabilities. A comprehensive review of these models is presented in this chapter.

Spontaneous symptomatic pseudoarthrosis at the T11-T12 intervertebral space with diffuse idiopathic skeletal hyperostosis: a case report.

STUDY DESIGN: We report on a 69-year-old male who had severe back pain due to spontaneous symptomatic pseudoarthrosis at the T11-T12 intervertebral space with diffuse idiopathic skeletal hyperostosis. OBJECTIVE: To describe a rare clinical entity and successful treatment by spinal fusion with a 4-year follow-up. SUMMARY OF BACKGROUND DATA: There have been a few reports of spontaneous symptomatic pseudoarthrosis of an intervertebral space associated with diffuse idiopathic skeletal hyperostosis, but there have been no reports of surgical treatment for this clinical condition. METHODS: Plain radiographs of the patient, who was admitted to our hospital with severe back pain but no history of trauma, revealed manifestations of diffuse idiopathic skeletal hyperostosis and a pseudoarthrosis at the T11-T12 intervertebral space. Posterior instrumentation from T9 to L2 and anterior bone grafting at the T11-T12 intervertebral space were performed. RESULTS: The patient has been followed for 4 years and is currently asymptomatic. CONCLUSIONS: A rare case of spontaneous symptomatic pseudoarthrosis at the T11-T12 intervertebral space with diffuse idiopathic skeletal hyperostosis was treated successfully by spinal fusion.