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Govindan Subramanian

Publications and source records attributed to Govindan Subramanian.

5 recordsLinked to original sources
PubMed2006

Computational approaches for modeling human intestinal absorption and permeability.

Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.

Caco-2 Cells↗
PubMed2003

Computational models to predict blood-brain barrier permeation and CNS activity.

The blood-brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood-brain partitioning (logBB) were captured through statistically significant quantitative structure-activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r approximately 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (approximately 60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline database was estimated. The results were very similar to the smaller set of 181 compounds.

Blood-Brain Barrier↗
PubMed2002

Cation-pi interactions: an energy decomposition analysis and its implication in delta-opioid receptor-ligand binding.

The nature and strength of the cation-pi interaction in protein-ligand binding are modeled by considering a series of nonbonded complexes involving N-substituted piperidines and substituted monocylic aromatics that mimic the delta-opioid receptor-ligand binding. High-level ab initio quantum mechanical calculations confirm the importance of such cation-pi interactions, whose intermolecular interaction energy ranges from -6 to -12 kcal/mol. A better understanding of the electrostatics, polarization, and other intermolecular interactions is obtained by appropriately decomposing the total interaction energy into their individual components. The energy decomposition analysis is also useful for parametrizing existing molecular mechanics force fields that could then account for energetic contributions arising out of cation-pi interactions in biomolecules. The present results further provide a framework for interpreting experimental results from point mutation reported for the delta-opioid receptor.

Cations↗