Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods

Computational modelling of solvent effects

This thesis is concerned with developing theoretical benchmarks and computational procedures that would facilitate robust descriptions of solvent effects on molecular properties and chemical reactions. This advancement will enable chemists to design more effective chemical reagents, drug molecules and materials, thereby reducing the need for extensive experimental trial-and-error. Towards this end, this thesis has developed theoretical benchmarks to evaluate the performance of lower-cost and approximate methods in predicting solute-solvent interaction energies. This includes the generation of high-level calculations of solute-solvent interactions and proton transfer reaction energies in very large water clusters (up to 160 water molecules) at a variety of solute-solvent configurations. This differs from previous studies, which mostly focused on small solvated clusters (1-6 solvent molecules) at equilibrium geometries. These theoretical benchmarks were then used to assess the performance of a range of contemporary density functional theory methods and hybrid quantum mechanics/molecular mechanics (QM/MM) approximations of these methods. A surprising finding was that significantly larger than expected QM region size (solute plus 40 or more water molecules) was needed before the QM/MM models converged to within 5.7 kJ mol-1 of the direct QM result. To address this limitation, an important contribution of this thesis is the development of efficient strategies based on charge-shift analysis and electrostatically embedded fragment methods to accelerate the convergence of the QM/MM models with respect to QM region size. Of particular note, the QM region selection based on atomic charges significantly reduced the errors in QM/MM models even when a low-level embedding potential was used. Finally, these findings culminated in developing a dual-Hamiltonian approach that may be used to systematically improve the accuracy of force field explicit solvent simulations of barriers of organic reactions. It is envisaged that these developments will directly contribute to the development of a systematic framework for improving computational simulations of solution-phase processes

New molecular simulation methods for quantitative modelling of protein-ligand interactions

The main theme of this work is the design and development of new molecular simulation protocols, to achieve more accurate and reliable estimates of free energy changes for processes relevant to the structure-based drug design. The works starts with an insight into the reproducibility problem for alchemical free energy calculations. Even if simulations are run with similar input files, the use of different simulation engines could give different free energy results. As part of a collaborative effort, the implementation details of AMBER, GROMACS, SOMD and CHARMM simulation codes were studied and free energy protocols for each software were validated to converge towards a reproducibility limit of about 0.20 kcal.mol-1 for hydration free energies of small organic molecules. Following, new simulation methods for the estimation of lipophilicity coefficients (log P and log D) for drug like molecules were developed and validated. log P values were computed for a dataset of 5 molecules with increasing fluorination level. Predictions were in line with the experimental measures and the simulations also allowed new insights into the water-solute interactions that drive the partitioning process. Then, as part of the SAMPL5 challenge, log D values for 53 drug-like molecules were computed. In this context two different simulation models were derived in order to take into account the presence of protonated species. The results were encouraging but also highlighted limits in alchemical free energy modelling. As an additional task of the SAMPL5 contest, three different protocols were validated for predicting absolute binding affinities for 22 host-guest systems. The first model yielded a free energy of binding based on free energy changes in solvated and complex phase; the second added the long range dispersion correction to the previous model; the third one used a standard state correction term. All three protocols were among the top-ranked submission in SAMPL5, with a correlation coefficient R2 of about 0.7 against experimental data. Finally, the origins and magnitude of the finite size artefacts in alchemical free energy calculations were investigated. Finite size artefacts are especially predominant in calculations that involve changes in the net-charge of a solute. A new correction scheme was devised for the Barker Watts Reaction Field approach and compared with the literature. Hydration free energy calculations on simple ionic species were carried out to validate the consistency of the scheme and the approach was further extended to host-guest binding affinities predictions

Nanomedicine Formulations Based on PLGA Nanoparticles for Diagnosis, Monitoring and Treatment of Disease: From Bench to Bedside

Nanomedicine is among the most promising emerging fields that can provide innovative and radical solutions to unmet needs in pharmaceutical formulation development. Encapsulation of active pharmaceutical ingredients within nano-size carriers offers several benefits, namely, protection of the therapeutic agents from degradation, their increased solubility and bioavailability, improved pharmacokinetics, reduced toxicity, enhanced therapeutic efficacy, decreased drug immunogenicity, targeted delivery, and simultaneous imaging and treatment options with a single system.Poly(lactide-co-glycolide) (PLGA) is one of the most commonly used polymers in nanomedicine formulations due to its excellent biocompatibility, tunable degradation characteristics, and high versatility. Furthermore, PLGA is approved by the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) for use in pharmaceutical products. Nanomedicines based on PLGA nanoparticles can offer tremendous opportunities in the diagnosis, monitoring, and treatment of various diseases.This Special Issue aims to focus on the bench-to-bedside development of PLGA nanoparticles including (but not limited to) design, development, physicochemical characterization, scale-up production, efficacy and safety assessment, and biodistribution studies of these nanomedicine formulations