Comparison of two methods for predicting aqueous solubility.

This study compares the solubility predictions of the two parameter general solubility equation (GSE) of Jain and Yalkowsky with the 171 parameter Klopman group contribution approach. Melting points and partition coefficients were obtained for each of the compounds from Klopman's test set. Using these two variables, the solubility of each compound was calculated by the GSE and compared to the values predicted by Klopman. Both methods give reasonable solubility predictions. The data of Klopman produced an average absolute error (AAE) of 0.71 and a root-mean-square error (RMSE) of 0.86, while the GSE had an AAE of 0.64 and a RMSE of 0.92.     

Estimation of aqueous solubility in drug design

The solubility of drugs in water is of central importance in the process of drug discovery and development from molecular design to pharmaceutical formulation and biopharmacy. The ability to estimate the aqueous solubility and other properties of a promising lead compound affecting its pharmacokinetics is a prerequisite to rational drug design, although it has received much less attention than the prediction of drug-receptor interactions. In this review, methods for the estimation of aqueous solubility of organic compounds are described and limited to approaches, which might be used in the early stage of drug design and development.     

Molecular Simulation and Statistical Learning Methods toward Predicting Drug–Polymer Amorphous Solid Dispersion Miscibility,Stability, and Formulation Design

Amorphous solid dispersions (ASDs) have emerged as widespread formulations for drug delivery of poorly soluble active pharmaceutical ingredients (APIs). Predicting the API solubility with various carriers in the API–carrier mixture and the principal API–carrier non-bonding interactions are critical factors for rational drug development and formulation decisions. Experimental determination of these interactions, solubility, and dissolution mechanisms is time-consuming, costly, and reliant on trial and error. To that end, molecular modeling has been applied to simulate ASD properties and mechanisms. Quantum mechanical methods elucidate the strength of API–carrier non-bonding interactions, while molecular dynamics simulations model and predict ASD physical stability, solubility, and dissolution mechanisms. Statistical learning models have been recently applied to the prediction of a variety of drug formulation properties and show immense potential for continued application in the understanding and prediction of ASD solubility. Continued theoretical progress and computational applications will accelerate lead compound development before clinical trials. This article reviews in silico research for the rational formulation design of low-solubility drugs. Pertinent theoretical groundwork is presented, modeling applications and limitations are discussed, and the prospective clinical benefits of accelerated ASD formulation are envisioned.     

Urea-amide preferential interactions in water: quantitative comparison of model compound data with biopolymer results using water accessible surface areas

Two fundamentally different thermodynamic approaches are in use to interpret or predict the effects of urea on biopolymer processes: one is a synthesis of transfer free energies obtained from measurements of the effects of urea on the solubilities of small, model compounds; the other is an analysis of preferential interactions of urea with a range of folded and unfolded biopolymer surfaces. Here, we compare the predictions of these two approaches for the contribution of urea-amide (peptide) interactions to destabilization of folded proteins by urea. For these comparisons, we develop independent thermodynamic analyses of osmometric and solubility data characterizing interactions of a model compound with urea (or any other solute) and apply them to all five model compounds (glycine, alanine, diglycine, glycylalanine, and triglycine) where both isopiestic distillation (ID) and solubility data in aqueous urea solutions are available. We use model-independent expressions to calculate mu ex 23, the derivative of the "excess" chemical potential of solute "2" (either a model compound or a biopolymer) with respect to the molality of solute "3" (urea). Analyses of ID data for these systems reveal significant dependences of mu ex 23 on both m2 and m3, which must be taken into account in making comparisons with values of mu ex 23 obtained from solubility studies or from analyses of urea-biopolymer preferential interactions. Values of mu ex 23 calculated from model compound ID data at low m2 and m3 are directly proportional to the amount of polar amide (N, O) surface area, and not to any other type of surface. The proportionality constant in this limit, mu ex 23 /(RT x ASA) = (1.0 +/- 0.1) x 10(-3) A(-2), is very similar to that previously obtained by analysis of urea-biopolymer preferential interactions ((1.4 +/- 0.3) x 10(-3) A(-2)). This level of agreement for amide surface in the low concentration limit, as well as the absence of any significant preferential interaction of urea with Gly and Ala, reinforces the conclusion that the primary preferential interaction of urea with protein surface is a favorable interaction (resulting in local accumulation of urea) at polar amide surface, located mostly on the peptide backbone. However, mu ex 23 for interactions of urea with these model amides is found from both ID and solubility data to be urea concentration-dependent, in contrast to the urea concentration independence of the analogous quantity for protein unfolding.     

Prediction of pH-dependent aqueous solubility of druglike molecules

In the present work, the Henderson-Hasselbalch (HH) equation has been employed for the development of a tool for the prediction of pH-dependent aqueous solubility of drugs and drug candidates. A new prediction method for the intrinsic solubility was developed, based on artificial neural networks that have been trained on a druglike PHYSPROP subset of 4548 compounds. For the prediction of acid/base dissociation coefficients, the commercial tool Marvin has been used, following validation on a data set of 467 molecules from the PHYSPROP database. The best performing network for intrinsic solubility predictions has a cross-validated root mean square error (RMSE) of 0.70 log S-units, while the Marvin pKa plug-in has an RMSE of 0.71 pH-units. A data set of 27 drugs with experimentally determined pH-solubility curves was assembled from the literature for the validation of the combined pH-dependent model, giving a mean RMSE of 0.79 log S-units. Finally, the combined model has been applied on profiling the solubility space at low pH of five large vendor libraries.     

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.     

Can we really do computer-aided drug design?

In this article, we discuss what we mean by ‘design’ and contrast this with the application of computational methods in drug discovery. We suggest that the predictivity of the computational models currently applied in drug discovery is not yet sufficient to permit a true design paradigm, as demonstrated by the large number of compounds that must currently be synthesised and tested to identify a successful drug. However, despite the uncertainties in predictions, computational methods have enormous potential value in narrowing the range of compounds to consider, by eliminating those that have negligible chance of being a successful drug, while focussing efforts on chemistries with the best likelihood of success. Applied appropriately, computational approaches can support decision-makers in achieving multi-parameter optimisation to guide the selection and design of compounds with the best chance of achieving an appropriate balance of properties for a drug discovery project’s objectives. Finally, we consider some approaches that may contribute over the next 25 years to improve the accuracy and transferability of computational models in drug discovery and move towards a genuine design process.     

DEGAS: sharing and tracking target compound ideas with external collaborators

To minimize the risk of failure in clinical trials, drug discovery teams must propose active and selective clinical candidates with good physicochemical properties. An additional challenge is that today drug discovery is often conducted by teams at different geographical locations. To improve the collaborative decision making on which compounds to synthesize, we have implemented DEGAS, an application which enables scientists from Genentech and from collaborating external partners to instantly access the same data. DEGAS was implemented to ensure that only the best target compounds are made and that they are made without duplicate effort. Physicochemical properties and DMPK model predictions are computed for each compound to allow the team to make informed decisions when prioritizing. The synthesis progress can be easily tracked. While developing DEGAS, ease of use was a particular goal in order to minimize the difficulty of training and supporting remote users.     

Applications of high throughput microsomal stability assay in drug discovery

High throughput in vitro microsomal stability assays are widely used in drug discovery as an indicator for in vivo stability, which affects pharmacokinetics. This is based on in-depth research involving a limited number of model drug-like compounds that are cleared predominantly by cytochrome P450 metabolism. However, drug discovery compounds are often not drug-like, are assessed with high throughput assays, and have many potential uncharacterized in vivo clearance mechanisms. Therefore, it is important to determine the correlation between high throughput in vitro microsomal stability data and abbreviated discovery in vivo pharmacokinetics study data for a set of drug discovery compounds in order to have evidence for how the in vitro assay can be reliably applied by discovery teams for making critical decisions. In this study the relationship between in vitro single time point high throughput microsomal stability and in vivo clearance from abbreviated drug discovery pharmacokinetics studies was examined using 306 real world drug discovery compounds. The results showed that in vitro Phase I microsomal stability t(1/2) is significantly correlated to in vivo clearance with a p-value<0.001. For compounds with low in vitro rat microsomal stability (t(1/2)<15 min), 87% showed high clearance in vivo (CL>25 mL/min/kg). This demonstrates that high throughput microsomal stability data are very effective in identifying compounds with significant clearance liabilities in vivo. For compounds with high in vitro rat microsomal stability (t(1/2)>15 min), no significant differentiation was observed between high and low clearance compounds. This is likely owing to other clearance pathways, in addition to cytochrome P450 metabolism that enhances in vivo clearance. This finding supports the strategy used by medicinal chemists and drug discovery teams of applying the in vitro data to triage compounds for in vivo PK and efficacy studies and guide structural modification to improve metabolic stability. When in vitro and in vivo data are both available for a compound, potential in vivo clearance pathways can be diagnosed to guide further discovery studies.     

大环化合物作为药物增溶剂的应用

药物研发的过程中,越来越多的化合物存在溶解性低的问题,因此提高药物溶解度是目前迫切需要解决的问题.环糊精、杯芳烃、葫芦脲等大环化合物可以通过主客体作用形成包合物从而增加难溶性药物的水溶性.本文介绍了几种大环化合物在药物增溶领域的应用.首先,基于大环化合物化学结构和空腔属性的差异,列举了它们可增溶药物的种类和范围;其次,...     

A cosolvency model to predict solubility of drugs at several temperatures from a limited number of solubility measurements

A cosolvency model to predict the solubility of drugs at several temperatures was derived from the excess free energy model of Williams and Amidon. The solubility of oxolinic acid, an antibacterial drug, was measured in aqueous (water+ethanol) and non-aqueous (ethanol+ethyl acetate) mixtures at several temperatures (20, 30, 35, 40 degrees C). Oxolinic acid displays a solubility maximum in each solvent mixture at solubility parameter values of 32 and 22 MPa(1/2). The temperature and heat of fusion were determined from differential scanning calorimetry. The solvent mixtures do not produce any solid phase change during the solubility experiments. The experimental results and those from the literature were employed to examine the accuracy and prediction capability of the proposed model. An equation was obtained to represent the drug solubility changes with cosolvent concentration and temperature. The model was also tested using a small number of experimental solubilities at 20 and 40 degrees C showing reasonably accurate predictions. This is important in pharmaceutics because it save experiments that are often expensive and time consuming.     

Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring

Ebola virus (EBOV) causes zoonotic viral infection with a potential risk of global spread and a highly fatal effect on humans. Till date, no drug has gotten market approval for the treatment of Ebola virus disease (EVD), and this perhaps allows the use of both experimental and computational approaches in the antiviral drug discovery process. The main target of potential vaccines that are recently undergoing clinical trials is trimeric glycoprotein (GP) of the EBOV and its exact crystal structure was used in this structure based virtual screening study, with the aid of consensus scoring to select three possible hit compounds from about 36 million compounds in MCULE’s database. Amongst these three compounds, (5R)-5-[[5-(4-chlorophenyl)-1,2,4-oxadiazol-3-yl]methyl]-N-[(4-methoxyphenyl)methyl]-4,5-dihydroisoxazole-3-carboxamide (SC-2, C₂₁H₁₉ClN₄O₄) showed good features with respect to drug likeness, ligand efficiency metrics, solubility, absorption and distribution properties and non-carcinogenicity to emerge as the most promising compound that can be optimized to lead compound against the GP EBOV. The binding mode showed that SC-2 is well embedded within the trimeric chains of the GP EBOV with molecular interactions with some amino acids. The SC-2 hit compound, upon its optimization to lead, might be a good potential candidate with efficacy against the EBOV pathogen and subsequently receive necessary approval to be used as antiviral drug for the treatment of EVD.     

Use of GAMESS/COSMO program in support of COSMO-SAC model applications in phase equilibrium prediction calculations

Thermodynamic models based on conductor-like screening models (COSMO) offer viable alternatives to existing group-contribution methods for the prediction of phase equilibria. Normally a COSMO-based model requires input of the distribution of screening charges on the molecular surface, aka. the sigma profile, determined from a specific quantum chemistry program and settings. For example, the COSMO-SAC model requires input of DMol³ generated sigma profiles. In this paper, we investigate the proper settings for an open-source quantum chemistry package GAMESS in order to generate sigma profiles to be used directly in the COSMO-SAC model. The phase behaviors (VLE and VLLE) of 45 binary mixtures from 10 commonly used solvents and the solubilities of 4 complex drug compounds in these solvents calculated from DMol³ and GAMESS generated sigma profiles are compared. While noticeable fine structure differences are observed in the individual sigma profiles for the same chemical compound generated from the two packages, it is found that the accuracy in the VLE/VLLE and solubility predictions from the two packages are comparable. Based on the systems we studied here, the open-source GAMESS/COSMO program with proper program settings could be used as an alternative sigma profile generation source in support of COSMO-SAC model applications in phase equilibrium prediction calculations.     

An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents

Prediction of solubility of active pharmaceutical ingredients (API) in different solvents is one of the main issue for crystallization process design. Experimental determination is not always possible because of the small amount of product available in the early stages of a drug development. Thus, one interesting perspective is the use of thermodynamic models, which are usually employed for predicting the activity coefficients in case of Vapour-Liquid equilibria or Liquid-Liquid equilibria (VLE or LLE). The choice of the best thermodynamic model for Solid-Liquid equilibria (SLE) is not an easy task as most of them are not meant particularly for this. In this paper, several models are tested for the solubility prediction of five drugs or drug-like molecules: Ibuprofen, Acetaminophen, Benzoic acid, Salicylic acid and 4-aminobenzoic acid, and another molecule, anthracene, a rather simple molecule. The performance of predictive (UNIFAC, UNIFAC mod., COSMO-SAC) and semi-predictive (NRTL-SAC) models are compared and discussed according to the functional groups of the molecules and the selected solvents. Moreover, the model errors caused by solid state property uncertainties are taken into account. These errors are indeed not negligible when accurate quantitative predictions want to be performed. It was found that UNIFAC models give the best results and could be an useful method for rapid solubility estimations of an API in various solvents. This model achieves the order of magnitude of the experimental solubility and can predict in which solvents the drug will be very soluble, soluble or not soluble. In addition, predictions obtained with NRTL-SAC model are also in good agreement with the experiments, but in that case the relevance of the results is strongly dependent on the model parameters regressed from solubility data in single and mixed solvents. However, this is a very interesting model for quick estimations like UNIFAC models. Finally, COSMO-SAC needs more developments to increase its accuracy especially when hydrogen bonding is involved. In that case, the predicted solubility is always overestimated from two to three orders of magnitude. Considering the use of the most accurate equilibrium equation involving the ΔC_p term, no benefits were found for drug predictions as the models are still too inaccurate. However, in function of the molecules and their solid thermodynamic properties, the ΔC_p term can be neglected and will not have a great impact on the results.