Prediction of the aqueous solubility of benzylamine salts using QSPR model

UiO-type metal-organic frameworks with NHC or metal-NHC functionalities for N-methylation using CO2 as the carbon source

We demonstrate the first metal-organic framework (MOF) that catalyzes N-methylation of amines using 1 atm CO2 and phenylsilane under ambient conditions. Compared with its homogeneous analog, the incorporation of N-heterocyclic carbene (NHC) into the MOF provides more efficient catalysis with improved reaction kinetics, turnover numbers and recyclability. Moreover, the metalated NHC functionalized MOF achieves direct N-methylation of amines bearing carboxylate moieties, which are common building blocks in pharmaceutical chemistry.

Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels

Quantitative structure-activity relationship (QSAR) studies and mechanistic mathematical modeling approaches have been independently employed for analysing and predicting the transport and distribution of small molecule chemical agents in living organisms. Both of these computational approaches have been useful for interpreting experiments measuring the transport properties of small molecule chemical agents, in vitro and in vivo. Nevertheless, mechanistic cell-based pharmacokinetic models have been especially useful to guide the design of experiments probing the molecular pathways underlying small molecule transport phenomena. Unlike QSAR models, mechanistic models can be integrated from microscopic to macroscopic levels, to analyse the spatiotemporal dynamics of small molecule chemical agents from intracellular organelles to whole organs, well beyond the experiments and training data sets upon which the models are based. Based on differential equations, mechanistic models can also be integrated with other differential equations-based systems biology models of biochemical networks or signaling pathways. Although the origin and evolution of mathematical modeling approaches aimed at predicting drug transport and distribution has occurred independently from systems biology, we propose that the incorporation of mechanistic cell-based computational models of drug transport and distribution into a systems biology modeling framework is a logical next step for the advancement of systems pharmacology research.

Analysis of relationships between solid-state properties, counterion, and developability of pharmaceutical salts

The solid-state properties of pharmaceutical salts, which are dependent on the counterion used to form the salt, are critical for successful development of a stable dosage form. In order to better understand the relationship between counterion and salt properties, 11 salts of procaine, which is a base, were synthesized and characterized using a variety of experimental and computational methods. Correlations between the various experimental and calculated physicochemical properties of the salts and counterions were probed. In addition to investigating the key factors affecting solubility, the hygroscopicity of the crystalline salts was studied to determine which solid-state and counterion properties might be responsible for enhancements in moisture uptake, thus providing the potential for adverse chemical stability. Multivariate principal components and partial least squares projection to latent structures analyses were performed in an attempt to establish predictive models capable of describing the relationships between these characteristics and both measured and calculated properties of the counterion and salt. Some success was achieved with respect to modeling crystalline salt solubility and the glass transition temperature of the amorphous salts. Through the modeling, insight into the relative importance of various descriptors on salt properties was achieved. The solid-state properties of crystalline and amorphous salts of procaine are highly dependent on the nature of the counterion. Important properties including aqueous solubility, melting point, hygroscopicity, and glass transition temperature were found to vary considerably between the different salts.

A new strategy of outlier detection for QSAR/QSPR

The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte-Carlo cross-validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross-predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods.

Pharmacokinetics in drug discovery

The aim of this current review is to summarize the present status of pharmacokinetics in Drug Discovery. The review is structured into four sections. The first section is a general overview of what we understand by pharmacokinetics and the different LADMET aspects: Liberation, Absorption, Distribution, Metabolism, Excretion, and Toxicity. The second section highlights the different computational or in silico approaches to estimate/predict one or several aspects of the pharmacokinetic profile of a discovery lead compound. The third section discusses the most commonly used in vitro methodologies. The fourth and last section examines the various approaches employed towards the pharmacokinetic assessment of discovery molecules; including all the LADME processes, discussing the different mathematical methodologies available to establish the PK profile of a test compound; what the main differences are and what should be the criteria for using one or another mathematical approach. The major conclusion of this review is that the use of the appropriate preclinical assays has a key role in the long-term viability of a pharmaceutical company since applying the right tools early in discovery will play a key role in determining the company's ability to discover novel safe and effective therapeutics to patients as quickly as possible.

Prediction of micelle–water partition coefficient from the theoretical derived molecular descriptors

The micelle-water partition coefficients of 81 organic compounds in SDS solution were predicted by quantitative structure-property relationship method. The multiple linear regression (MLR) and artificial neural network (ANN) techniques were used to build linear and nonlinear model, respectively. In this work the proposed QSPR models, both by MLR and ANN, contain identical descriptors which are zero order of Kier-Hall index, count of Hydrogen donors site [Zefirovs PC], average valency of a C atom, atomic charge weighted by partial positively charged surface area and minimum one electron reaction index for a C atom. The MLR model gave a root mean square (RMS) of 0.166, 0.25, and 0.289 for training, prediction and test sets, respectively, whereas ANN gave an RMS error of 0.06, 0.21, and 0.20 for training, prediction, and test sets, respectively. Comparison the results of these two methods reveals that those obtained by the ANN model are much better.

Relationship between physicochemical properties, lipophilicity parameters, and local anesthetic activity of dibasic esters of phenylcarbamic acid

The basic physicochemical properties, lipophilicity parameters of dibasic alkyloxy-substituted phenylcarbamic acids were estimated. For the prepared set of compounds the experimentally obtained solubility, acidity, and lipophilicity parameters were correlated with those computed using various computer programs based on the associative artificial neural network and fragmental methods. The results of pharmacological evaluation were used as entry data for the complex correlations.

Recent progress in the computational prediction of aqueous solubility and absorption

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties.