Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database

The accurate prediction of solubility of drugs is still problematic. It was thought for a long time that shortfalls had been due the lack of high-quality solubility data from the chemical space of drugs. This study considers the quality of solubility data, particularly of ionizable drugs. A database is described, comprising 6355 entries of intrinsic solubility for 3014 different molecules, drawing on 1325 citations. In an earlier publication, many factors affecting the quality of the measurement had been discussed, and suggestions were offered to improve ways of extracting more reliable information from legacy data. Many of the suggestions have been implemented in this study. By correcting solubility for ionization (i.e., deriving intrinsic solubility, S0) and by normalizing temperature (by transforming measurements performed in the range 10-50 °C to 25 °C), it can now be estimated that the average interlaboratory reproducibility is 0.17 log unit. Empirical methods to predict solubility at best have hovered around the root mean square error (RMSE) of 0.6 log unit. Three prediction methods are compared here: (a) Yalkowsky’s general solubility equation (GSE), (b) Abraham solvation equation (ABSOLV), and (c) Random Forest regression (RFR) statistical machine learning. The latter two methods were trained using the new database. The RFR method outperforms the other two models, as anticipated. However, the ability to predict the solubility of drugs to the level of the quality of data is still out of reach. The data quality is not the limiting factor in prediction. The statistical machine learning methodologies are probably up to the task. Possibly what’s missing are solubility data from a few sparsely-covered chemical space of drugs (particularly of research compounds). Also, new descriptors which can better differentiate the factors affecting solubility between molecules could be critical for narrowing the gap between the accuracy of the prediction models and that of the experimental data

Can small drugs predict the intrinsic aqueous solubility of ‘beyond Rule of 5’ big drugs?

The aim of the study was to explore to what extent small molecules (mostly from the Rule of 5 chemical space) can be used to predict the intrinsic aqueous solubility, S0, of big molecules from beyond the Rule of 5 (bRo5) space. It was demonstrated that the General Solubility Equation (GSE) and the Abraham Solvation Equation (ABSOLV) underpredict solubility in systematic but slightly ways. The Random Forest regression (RFR) method predicts solubility more accurately, albeit in the manner of a ‘black box.’ It was discovered that the GSE improves considerably in the case of big molecules when the coefficient of the log P term (octanol-water partition coefficient) in the equation is set to -0.4 instead of the traditional -1 value. The traditional GSE underpredicts solubility for molecules with experimental S0 < 50 µM. In contrast, the ABSOLV equation (trained with small molecules) underpredicts the solubility of big molecules in all cases tested. It was found that the errors in the ABSOLV-predicted solubilities of big molecules correlate linearly with the number of rotatable bonds, which suggests that flexibility may be an important factor in differentiating solubility of small from big molecules. Notably, most of the 31 big molecules considered have negative enthalpy of solution: these big molecules become less soluble with increasing temperature, which is compatible with ‘molecular chameleon’ behavior associated with intramolecular hydrogen bonding. The X‑ray structures of many of these molecules reveal void spaces in their crystal lattices large enough to accommodate many water molecules when such solids are in contact with aqueous media. The water sorbed into crystals suspended in aqueous solution may enhance solubility by way of intra-lattice solute-water interactions involving the numerous H‑bond acceptors in the big molecules studied. A ‘Solubility Enhancement–Big Molecules’ index was defined, which embodies many of the above findings.</p

Fast and general method to predict the physicochemical properties of druglike molecules using the integral equation theory of molecular liquids

We report a method to predict physico-chemical properties of druglike molecules using a classical statistical mechanics based solvent model combined with machine learning. The RISM-MOL-INF method introduced here provides an accurate technique to characterize solvation and desolvation processes based on solute-solvent correlation functions computed by the 1D Reference Interaction Site Model of the Integral Equation Theory of Molecular Liquids. These functions can be obtained in a matter of minutes for most small organic and druglike molecules using existing software (RISM-MOL) (Sergiievskyi, V. P.; Hackbusch, W.; Fedorov, M. V. J. Comput. Chem. 2011, 32, 1982-1992.). Predictions of caco-2 cell permeability and hydration free energy obtained using the RISM-MOL-INF method are shown to be more accurate than the state-of-the-art tools for benchmark datasets. Due to the importance of solvation and desolvation effects in biological systems, it is anticipated that the RISM-MOL-INF approach will find many applications in biophysical and biomedical property prediction

Machine Learning predicts the effect of food on orally administered medicines

Food-mediated changes to drug absorption, termed the food effect, are hard to predict and can have significant implications for the safety and efficacy of oral drug products in patients. Mimicking the prandial states of the human gastrointestinal tract in preclinical studies is challenging, poorly predictive and can produce difficult to interpret datasets. Machine learning (ML) has emerged from the computer science field and shows promise in interpreting complex datasets present in the pharmaceutical field. A ML-based approach aimed to predict the food effect based on an extensive dataset of over 311 drugs with more than 20 drug physicochemical properties, referred to as features. Machine learning techniques were tested; including logistic regression, support vector machine, k-Nearest neighbours and random forest. First a standard ML pipeline using a 80:20 split for training and testing was tried to predict no food effect (F0), negative food effect (F-) and positive food effect (F+), however this lead to specificities of less than 40%. To overcome this, a strategic ML pipeline was devised and three tasks were developed. Random forest achieved the strongest performance overall. High accuracies and sensitivities of 70%, 80% and 70% and specificities of 71%, 76% and 71% were achieved for classifying; (i) no food effect vs food effect, (ii) negative food vs positive food effect and (iii) no food effect vs negative food effect vs positive food effect, respectively. Feature importance using random forest ranked the features by importance for building the predictive tasks. The calculated dose number was the most important feature. Here, ML has provided an effective screening tool for predicting the food effect, with the potential to select lead compounds with no food effect, reduce the number of animal studies, and accelerate oral drug development studies

Control of crystalline particle properties by spray drying

Although spray drying has been common place in the pharmaceutical industry for decades, the integration of the technique into continuous manufacturing can offer an extensive array of particle engineering applications. Continuous manufacturing aims to deliver consistent and sustainable drug products of a better and higher quality. Spray drying is a continuous processing technique typically adopted for amorphous solid production. However, the unique conditions of the technique can also can be adapted and applied to crystallisation enabling particle property engineering. The semi-continuous lab-scale Büchi B-290 Mini spray dryer is widely available and has been previously studied extensively for particle engineering and as a development platform for applications including pulmonary drug delivery, sustained release formulations and amorphous solid dispersions.The focus of this work is to engineer and enhance particle properties through the use of the Büchi spray dryer. Particle formation has been investigated, with specific focus in terms of polymorph formation in carbamazepine, to develop a predictive model for crystallisability and for co-spray drying of metformin hydrochloride with mannitol and lactose. Particle formation has been described in terms of theoretical drying kinetics and combined with off line characterisation to determine size and form of product. The metastable polymorph, form IV, of carbamazepine was made reproducibly by spray drying with the combination of rapid evaporation and product isolation shown to be crucial to prevention of solution mediated transformation. The application of non-invasive Raman spectroscopy was also utilised to assess product form. A crystallisability predictive model based on a Random Forest method was successfully produced through combining molecular descriptors with published and experimental outcomes. The model provided up to 79 % accuracy in predicting whether an amorphous or crystalline product would be expected from rapid drying. This shows considerable utility in streamlining process development. Finally, co-spray drying in the Büchi system using a three-fluid nozzle was used to produce multicomponent composite particles comprising of two crystallite phases. The effect of process configuration and material properties on the resultant particles was assessed using particle sizing, SEM, XRPD and Raman mapping. The results were compared on the basis of theoretical drying kinetics to assess the ability to predict the resultant particle morphology. Four multicomponent composite particles were produced by co-spray drying from metformin hydrochloride (MF), mannitol and lactose. MF-mannitol composites produced three-phase physical mixtures with both components present on the particle surfaces. The particle surface compositions were contradictory to the expected particle outcomes from the drying parameters. MF-lactose composite particle also produce three-phase physical mixtures with a relatively equal distribution of components present on particle surface. This is consistent with the expected particle from the drying parameters. The different particle outcomes suggest that co-spray drying of miscible multicomponent feeds using the three-fluid nozzle is highly dependent on the drying parameters for each component due to equal mixing of the feed at atomisation of droplets.Although spray drying has been common place in the pharmaceutical industry for decades, the integration of the technique into continuous manufacturing can offer an extensive array of particle engineering applications. Continuous manufacturing aims to deliver consistent and sustainable drug products of a better and higher quality. Spray drying is a continuous processing technique typically adopted for amorphous solid production. However, the unique conditions of the technique can also can be adapted and applied to crystallisation enabling particle property engineering. The semi-continuous lab-scale Büchi B-290 Mini spray dryer is widely available and has been previously studied extensively for particle engineering and as a development platform for applications including pulmonary drug delivery, sustained release formulations and amorphous solid dispersions.The focus of this work is to engineer and enhance particle properties through the use of the Büchi spray dryer. Particle formation has been investigated, with specific focus in terms of polymorph formation in carbamazepine, to develop a predictive model for crystallisability and for co-spray drying of metformin hydrochloride with mannitol and lactose. Particle formation has been described in terms of theoretical drying kinetics and combined with off line characterisation to determine size and form of product. The metastable polymorph, form IV, of carbamazepine was made reproducibly by spray drying with the combination of rapid evaporation and product isolation shown to be crucial to prevention of solution mediated transformation. The application of non-invasive Raman spectroscopy was also utilised to assess product form. A crystallisability predictive model based on a Random Forest method was successfully produced through combining molecular descriptors with published and experimental outcomes. The model provided up to 79 % accuracy in predicting whether an amorphous or crystalline product would be expected from rapid drying. This shows considerable utility in streamlining process development. Finally, co-spray drying in the Büchi system using a three-fluid nozzle was used to produce multicomponent composite particles comprising of two crystallite phases. The effect of process configuration and material properties on the resultant particles was assessed using particle sizing, SEM, XRPD and Raman mapping. The results were compared on the basis of theoretical drying kinetics to assess the ability to predict the resultant particle morphology. Four multicomponent composite particles were produced by co-spray drying from metformin hydrochloride (MF), mannitol and lactose. MF-mannitol composites produced three-phase physical mixtures with both components present on the particle surfaces. The particle surface compositions were contradictory to the expected particle outcomes from the drying parameters. MF-lactose composite particle also produce three-phase physical mixtures with a relatively equal distribution of components present on particle surface. This is consistent with the expected particle from the drying parameters. The different particle outcomes suggest that co-spray drying of miscible multicomponent feeds using the three-fluid nozzle is highly dependent on the drying parameters for each component due to equal mixing of the feed at atomisation of droplets