"Flexible-Acceptor" General Solubility Equation for beyond Rule of 5 Drugs.

This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, S0, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis. The new approach replaces the GSE constant factors in the intercept (0.5), the octanol-water log P (-1.0), and melting point, mp (-0.01) terms with simple exponential functions incorporating the sum descriptor, Φ+B (Kier Φ molecular flexibility and Abraham H-bond acceptor potential). The constants in the modified three-variable (log P, mp, Φ+B) equation were determined by partial least-squares (PLS) refinement using a small-molecule log S0 training set (n = 6541) of mostly druglike molecules. In this "flexible-acceptor" GSE(Φ,B) model, the coefficient of log P (normally fixed at -1.0) varies smoothly from -1.1 for rigid nonionizable molecules (Φ+B = 0) to -0.39 for typically flexible (Φ âˆ¼ 20, B ∼ 6) large molecules. The intercept (traditionally fixed at +0.5) varies smoothly from +1.9 for completely inflexible small molecules to -2.2 for typically flexible large molecules. The mp coefficient (-0.007) remains practically constant, near the traditional value (-0.01) for most molecules, which suggests that the small-to-large molecule continuum is mainly solvation responsive, apparently with only minor changes in the crystal lattice contributions. For a test set of 32 large molecules (e.g., cyclosporine A, gramicidin A, leuprolide, nafarelin, oxytocin, vancomycin, and mostly natural-product-derived therapeutics used in infectious/viral diseases, in immunosuppression, and in oncology) the modified equation predicted the intrinsic solubility with a root-mean-square error of 1.10 log unit, compared to 3.0 by the traditional GSE, and 1.07 by RFR.

Passive lipoidal diffusion and carrier-mediated cell uptake are both important mechanisms of membrane permeation in drug disposition.

Recently, it has been proposed that drug permeation is essentially carrier-mediated only and that passive lipoidal diffusion is negligible. This opposes the prevailing hypothesis of drug permeation through biological membranes, which integrates the contribution of multiple permeation mechanisms, including both carrier-mediated and passive lipoidal diffusion, depending on the compound's properties, membrane properties, and solution properties. The prevailing hypothesis of drug permeation continues to be successful for application and prediction in drug development. Proponents of the carrier-mediated only concept argue against passive lipoidal diffusion. However, the arguments are not supported by broad pharmaceutics literature. The carrier-mediated only concept lacks substantial supporting evidence and successful applications in drug development.

From lin-benzoguanines to lin-benzohypoxanthines as ligands for Zymomonas mobilis tRNA-guanine transglycosylase: replacement of protein-ligand hydrogen bonding by importing water clusters.

The foodborne illness shigellosis is caused by Shigella bacteria that secrete the highly cytotoxic Shiga toxin, which is also formed by the closely related enterohemorrhagic Escherichia coli (EHEC). It has been shown that tRNA-guanine transglycosylase (TGT) is essential for the pathogenicity of Shigella flexneri. Herein, the molecular recognition properties of a guanine binding pocket in Zymomonas mobilis TGT are investigated with a series of lin-benzohypoxanthine- and lin-benzoguanine-based inhibitors that bear substituents to occupy either the ribose-33 or the ribose-34 pocket. The three inhibitor scaffolds differ by the substituent at C(6) being H, NH(2), or NH-alkyl. These differences lead to major changes in the inhibition constants, pK(a) values, and binding modes. Compared to the lin-benzoguanines, with an exocyclic NH(2) at C(6), the lin-benzohypoxanthines without an exocyclic NH(2) group have a weaker affinity as several ionic protein-ligand hydrogen bonds are lost. X-ray cocrystal structure analysis reveals that a new water cluster is imported into the space vacated by the lacking NH(2) group and by a conformational shift of the side chain of catalytic Asp102. In the presence of an N-alkyl group at C(6) in lin-benzoguanine ligands, this water cluster is largely maintained but replacement of one of the water molecules in the cluster leads to a substantial loss in binding affinity. This study provides new insight into the role of water clusters at enzyme active sites and their challenging substitution by ligand parts, a topic of general interest in contemporary structure-based drug design.

Predicting Solubility of Newly-Approved Drugs (2016-2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression.

This study applies the 'Flexible-Acceptor' variant of the General Solubility Equation, GSE(Φ,B), to the prediction of the aqueous intrinsic solubility, log10 S 0, of FDA recently-approved (2016-2020) 'small-molecule' new molecular entities (NMEs). The novel equation had been shown to predict the solubility of drugs beyond Lipinski's 'Rule of 5' chemical space (bRo5) to a precision nearly matching that of the Random Forest Regression (RFR) machine learning method. Since then, it was found that the GSE(Φ,B) appears to work well not only for bRo5 NMEs, but also for Ro5 drugs. To put context to GSE(Φ,B), Yalkowsky's GSE(classic), Abraham's ABSOLV, and Breiman's RFR models were also applied to predict log10 S 0 of 72 newly-approve NMEs, for which useable reported solubility values could be accessed (nearly 60% from FDA New Drug Application published reports). Except for GSE (classic), the prediction models were retrained with an enlarged version of the Wiki-pS 0 database (nearly 400 added log10 S 0 entries since our recent previous study). Thus, these four models were further validated by the additional independent solubility measurements which the newly-approved drugs introduced. The prediction methods ranked RFR ~ GSE (Φ,B) > ABSOLV > GSE (classic) in performance. It was further demonstrated that the biases generated in the four separate models could be nearly eliminated in a consensus model based on the average of just two of the methods: GSE (Φ,B) and ABSOLV. The resulting consensus prediction equation is simple in form and can be easily incorporated into spreadsheet calculations. Even more significant, it slightly outperformed the RFR method.

Comparative evaluation of in silico systems for ames test mutagenicity prediction: scope and limitations.

The predictive power of four commonly used in silico tools for mutagenicity prediction (DEREK, Toxtree, MC4PC, and Leadscope MA) was evaluated in a comparative manner using a large, high-quality data set, comprising both public and proprietary data (F. Hoffmann-La Roche) from 9,681 compounds tested in the Ames assay. Satisfactory performance statistics were observed on public data (accuracy, 66.4-75.4%; sensitivity, 65.2-85.2%; specificity, 53.1-82.9%), whereas a significant deterioration of sensitivity was observed in the Roche data (accuracy, 73.1-85.5%; sensitivity, 17.4-43.4%; specificity, 77.5-93.9%). As a general tendency, expert systems showed higher sensitivity and lower specificity when compared to QSAR-based tools, which displayed the opposite behavior. Possible reasons for the performance differences between the public and Roche data, relating to the experimentally inactive to active compound ratio and the different coverage of chemical space, are thoroughly discussed. Examples of peculiar chemical classes enriched in false negative or false positive predictions are given, and the results of the combined use of the prediction systems are described.

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.

The Critical Role of Passive Permeability in Designing Successful Drugs.

Passive permeability is a key property in drug disposition and delivery. It is critical for gastrointestinal absorption, brain penetration, renal reabsorption, defining clearance mechanisms and drug-drug interactions. Passive diffusion rate is translatable across tissues and animal species, while the extent of absorption is dependent on drug properties, as well as in vivo physiology/pathophysiology. Design principles have been developed to guide medicinal chemistry to enhance absorption, which combine the balance of aqueous solubility, permeability and the sometimes unfavorable compound characteristic demanded by the target. Permeability assays have been implemented that enable rapid development of structure-permeability relationships for absorption improvement. Future advances in assay development to reduce nonspecific binding and improve mass balance will enable more accurately measurement of passive permeability. Design principles that integrate potency, selectivity, passive permeability and other ADMET properties facilitate rapid advancement of successful drug candidates to patients.

Safety screening in early drug discovery: An optimized assay panel.

BACKGROUND: Several factors contribute to the development failure of novel pharmaceuticals, one of the most important being adverse effects in pre-clinical and clinical studies. Early identification of off-target compound activity can reduce safety-related attrition in development. In vitro profiling of drug candidates against a broad range of targets is an important part of the compound selection process. Many compounds are synthesized during early drug discovery, making it necessary to assess poly-pharmacology at a limited number of targets. This paper describes how a rational, statistical-ranking approach was used to generate a cost-effective, optimized panel of assays that allows selectivity focused structure-activity relationships to be explored for many molecules. This panel of 50 targets has been used to routinely screen Roche small molecules generated across a diverse range of therapeutic targets. Target hit rates from the Bioprint® database and internal Roche compounds are discussed. We further describe an example of how this panel was used within an anti-infective project to reduce in vivo testing. METHOD: To select the optimized panel of targets, IC50 values of compounds in the BioPrint® database were used to identify assay "hits" i.e. IC50 ≤ 1 µM in 123 different in vitro pharmacological assays. If groups of compounds hit the same targets, the target with the higher hit rate was selected, while others were considered redundant. Using a step-wise analysis, an assay panel was identified to maximize diversity and minimize redundancy. Over a five-year period, this panel of 50 off-targets was used to screen ≈1200 compounds synthesized for Roche drug discovery programs. Compounds were initially tested at 10 µM and hit rates generated are reported. Within one project, the number of hits was used to refine the choice of compounds being assessed in vivo. RESULTS: 95% of compounds from the BioPrint® panel were identified within the top 47-ranked assays. Based on this analytical approach and the addition of three targets with established safety concerns, a Roche panel was created for external screening. hERG is screened internally and not included in this analysis. Screening at 10 µM in the Roche panel identified that adenosine A3 and 5HT2B receptors had the highest hit rates (~30%), with 50% of the targets having a hit rate of ≤4%. An anti-infective program identified that a high number of hits in the Roche panel was associated with mortality in 19 mouse tolerability studies. To reduce the severity and number of such studies, future compound selections integrated the panel hit score into the selection process for in vivo studies. It was identified that compounds which hit less targets in the panel and had free plasma exposures of ~2 µM were generally better tolerated. DISCUSSION: This paper describes how an optimized panel of 50 assays was selected on the basis of hit similarity at 123 targets. This reduced panel, provides a cost-effective screening panel for assessing compound promiscuity, whilst also including many safety-relevant targets. Frequent use of the panel in early drug discovery has provided promiscuity and safety-relevant information to inform pre-clinical drug development at Roche.

Design, data analysis, and simulation of in vitro drug transport kinetic experiments using a mechanistic in vitro model.

The use of in vitro data for quantitative predictions of transporter-mediated elimination in vivo requires an accurate estimation of the transporter Michaelis-Menten parameters, V(max) and K(m), as a first step. Therefore, the experimental conditions of in vitro studies used to assess hepatic uptake transport were optimized regarding active transport processes, nonspecific binding, and passive diffusion (P(dif)). A mechanistic model was developed to analyze and accurately describe these active and passive processes. This two-compartmental model was parameterized to account for nonspecific binding, bidirectional passive diffusion, and active uptake processes based on the physiology of the cells. The model was used to estimate kinetic parameters of in vitro transport data from organic anion-transporting peptide model substrates (e.g., cholecystokinin octapeptide deltorphin II, fexofenadine, and pitavastatin). Data analysis by this mechanistic model significantly improved the accuracy and precision in all derived parameters [mean coefficient of variations (CVs) for V(max) and K(m) were 19 and 23%, respectively] compared with the conventional kinetic method of transport data analysis (mean CVs were 58 and 115%, respectively, using this method). Furthermore, permeability was found to be highly temperature-dependent in Chinese hamster ovary (CHO) control cells and artificial membranes (parallel artificial membrane permeability assay). Whereas for some compounds (taurocholate, estrone-3-sulfate, and propranolol) the effect was moderate (1.5-6-fold higher permeability at 37 degrees C compared with that at 4 degrees C), for fexofenadine a 16-fold higher passive permeability was seen at 37 degrees C. Therefore, P(dif) was better predicted if it was evaluated under the same experimental conditions as V(max) and K(m), i.e., in a single incubation of CHO overexpressed cells or rat hepatocytes at 37 degrees C, instead of a parallel control evaluation at 4 degrees C.

Computational aqueous solubility prediction for drug-like compounds in congeneric series.

It was the aim of the present work to develop a quantitative structure-property relationship (QSPR) model for predicting the aqueous solubility of drug-like compounds in congeneric series. Lipophilicity combined with structural fragment information, fragmental based correction factors and congeneric series indices were used as descriptors for a principal component analysis (PCA) followed by multivariate partial least squares regression statistics (PLS). The derived PLS regression model for the prediction of solubility parameters was based on an in-house data set of 2473 drug-like compounds. The generated PLS model had a coefficient of determination (R(2))=0.844 and a root-mean-square (rms) error of 0.51 log units. It predicted the solubility of the test data set with a high degree of accuracy (R(2)=0.81). In addition, the PLS model was successful in predicting the solubility of new congeneric test sets when solubility values of corresponding scaffolds were accessible.

In silico prediction of brain and CSF permeation of small molecules using PLS regression models.

Computational partial least square (PLS) regression models were developed, which can be applied to predict central nervous system (CNS) penetration of drug-like organic molecules. For modeling, a dataset of 77 structurally diverse compounds was used with reported steady-state rat brain to plasma ratios (BPR). Information on steady-state cerebrospinal fluid distribution (CSF to plasma ratio or CSFPR) was available for 37 of these compounds. The molecules were from different chemical series and included bases, acids, zwitterions and neutral molecules. They were CNS active and were therefore assumed to penetrate the blood-brain barrier and/or the blood-liquor barrier. Using these PLS models, the dataset could be described accurately (r(2)=0.78, StErrorEst=0.30 and r(2)=0.75, StErrorEst=0.28 for BPR and CSFPR, respectively). Molecular descriptors used for the prediction of passive membrane transport were lipophilicity, polar and hydrophobic surface areas as well as structural parameters and net charge at physiological pH. There was no apparent correlation between experimental brain and CSF exposure. Consequently, different PLS models and guiding rules were developed and discussed for the prediction of BPR or CSFPR. The present models provide a cost-effective and efficient strategy to guide synthetic efforts in medicinal chemistry at an early stage of the drug discovery and development process.

Absorption-excipient-pH classification gradient maps: sparingly soluble drugs and the pH partition hypothesis.

This study of sparingly soluble model drugs assesses (a) how pH and the aqueous boundary layer factors may affect in vitro and in vivo absorption, (b) to what extent single excipients (sodium taurocholate, hydroxypropyl-beta-cyclodextrin, KCl, propylene glycol, methylpyrrolidone, and polyethylene glycol 400) can mitigate adverse absorption effects, and (c) how a novel rank-order visualization tool can be applied in high-throughput screening to identify promising single-excipient effects on the absorption potential of test compounds. The products of accurately measured solubility and artificial-membrane permeability (PAMPA) values at pH 5.0, 6.2, and 7.4, fully taking into account factors such as aqueous boundary layer resistance, membrane retention, and the formation of drug dimers and trimers, were used to define a flux function. A "self-organized" data visualization tool based on the flux function was mined for the promising excipient-drug combinations. In excipient-free solutions, most of the compounds studied formed aggregates. The presence of an excipient predominantly lowered permeability, but most often not by the same amount as solubility was elevated. The compounds with absorption potential most helped by excipients were: clotrimazole>griseofulvin>progesterone>dipyridamole>glibenclamide>mefenamic acid>butacaine>astemizole. The HP-beta-CD effect observed for albendazole and glibenclamide appeared to follow Cmax trends in published pharmacokinetics studies. A surprising outcome of the in vitro measurements was that the classical pH Partition Hypothesis can be "inverted" in its monotonicity by sparingly soluble compounds.

High throughput solubility measurement in drug discovery and development.

Measurement of drug solubility in various solvents is one of the key elements of compound characterization during the whole discovery and development process. This review summarizes current experimental approaches and addresses recent advances in the experimental methods used to determine drug solubility in drug discovery and early development. This paper focuses on high throughput methods designed to determine kinetic and thermodynamic (equilibrium) solubility but traditional methods are also presented. The focus, positioning, experimental setup, pros and cons, and limitations of individual assays are discussed and differences in solubility studies in discovery and development environments are highlighted. Finally, future needs and trends in solubility assay development designed to overcome current bottlenecks and trade-offs between speed and quality/quantity of measurements are addressed.

PAMPA--critical factors for better predictions of absorption.

PAMPA, log P(OCT), and Caco-2 are useful tools in drug discovery for the prediction of oral absorption, brain penetration and for the development of structure-permeability relationships. Each approach has its advantages and limitations. Selection criteria for methods are based on many different factors: predictability, throughput, cost and personal preferences (people factor). The PAMPA concerns raised by Galinis-Luciani et al. (Galinis-Luciani et al., 2007, J Pharm Sci, this issue) are answered by experienced PAMPA practitioners, inventors and developers from diverse research organizations. Guidelines on how to use PAMPA are discussed. PAMPA and PAMPA-BBB have much better predictivity for oral absorption and brain penetration than log P(OCT) for real-world drug discovery compounds. PAMPA and Caco-2 have similar predictivity for passive oral absorption. However, it is not advisable to use PAMPA to predict absorption involving transporter-mediated processes, such as active uptake or efflux. Measurement of PAMPA is much more rapid and cost effective than Caco-2 and log P(OCT). PAMPA assay conditions are critical in order to generate high quality and relevant data, including permeation time, assay pH, stirring, use of cosolvents and selection of detection techniques. The success of using PAMPA in drug discovery depends on careful data interpretation, use of optimal assay conditions, implementation and integration strategies, and education of users.

Permeation of permanently positive charged molecules through artificial membranes--influence of physico-chemical properties.

The aim of this study was to investigate the permeation properties of 20 permanently positive charged molecules in the parallel artificial membrane permeability assay (PAMPA). Eight of them were derivatives of the N-alkyl-isoquinolinium salt and 12 were congeners of the dye rhodamine 110. Five out of 12 molecules from the rhodamine 110 series have one additional carboxylic group and two have two carboxylic acids. The experimentally derived effective permeability values (P(e)) cover a range of 3-4 log units. Ten compounds showed low permeabilities (P(e)<0.1x10(-6)cm/s), four medium permeabilities (0.1x10(-6)< or =P(e)<1x10(-6)cm/s) and six were highly permeable (P(e)> or =1x10(-6)cm/s). In addition, computational models were built with a number of calculated molecular descriptors and evaluated for their ability to predict membrane permeability. It turned out that the experimental P(e) values can be explained by electronic properties and parameters describing the shape of molecules. This work provides evidence that permanently charged molecules can have high passive membrane permeabilities provided that the charge can be spread over several aromatic ring systems.

A potentiometric titration method for the crystallization of drug-like organic molecules.

It is generally accepted, that crystalline solids representing a low energy polymorph should be selected for development of oral dosage forms. As a consequence, efficient and robust procedures are needed at an early stage during drug discovery to prepare crystals from drug-like organic molecules. In contrast to the use of supersaturated solutions, we present a potentiometric crystallization procedure where saturated solutions are prepared in a controlled manner by pH-titration. Crystallization is carried out under defined conditions using the sample concentration and experimental pK(a) values as input parameters. Crystals of high quality were obtained for 11 drugs selected to demonstrate the efficiency and applicability of the new method. Technical improvements are suggested to overcome practical limitations and to enhance the possibility of obtaining crystals from molecules in their uncharged form.

Lactam-Stapled Cell-Penetrating Peptides: Cell Uptake and Membrane Binding Properties.

Stapling of side chains to stabilize an α-helical structure has been generally associated with an increased uptake of CPPs. Here, we compare four amphiphilic stapled peptides with their linear counterparts in terms of their membrane binding and conformational features in order to correlate these with uptake efficiency and toxicological effects. The impact of lactam stapling was found to vary strongly with regard to the different aspects of peptide-membrane interactions. Nearly all stapled peptides caused less membrane perturbation (vesicle leakage, hemolysis, bacterial lysis) than their linear counterparts. In one case (MAP-1) where stapling enhanced α-helicity in aqueous and lipid environments, leakage was eliminated while cell uptake in HEK293 and HeLa cells remained high, which improved the overall characteristics. The other systems (DRIM, WWSP, KFGF) did not improve, however. The data suggest that cell uptake of amphipathic CPPs correlates with their adopted α-helix content in membranes rather than their helicity in solution.

Integrating molecular design resources within modern drug discovery research: the Roche experience.

Various computational disciplines, such as cheminformatics, ADME modeling, virtual screening, chemogenomics search strategies and classic structure-based design, should be seen as one multifaceted discipline contributing to the early drug discovery process. Although significant resources enabling these activities have been established, their true integration into daily research should not be taken for granted. This article reviews value-adding activities from target assessment to lead optimization, and highlights the technical and process-related aspects that can be considered essential for performance and alignment within the research organization.

Effect of Partially Fluorinated N-Alkyl-Substituted Piperidine-2-carboxamides on Pharmacologically Relevant Properties.

The modulation of pharmacologically relevant properties of N-alkyl-piperidine-2-carboxamides was studied by selective introduction of 1-3 fluorine atoms into the n-propyl and n-butyl side chains of the local anesthetics ropivacaine and levobupivacaine. The basicity modulation by nearby fluorine substituents is essentially additive and exhibits an exponential attenuation as a function of topological distance between fluorine and the basic center. The intrinsic lipophilicity of the neutral piperidine derivatives displays the characteristic response noted for partially fluorinated alkyl groups attached to neutral heteroaryl systems. However, basicity decrease by nearby fluorine substituents affects lipophilicities at neutral pH, so that all partially fluorinated derivatives are of similar or higher lipophilicity than their non-fluorinated parents. Aqueous solubilities were found to correlate inversely with lipophilicity with a significant contribution from crystal packing energies, as indicated by variations in melting point temperatures. All fluorinated derivatives were found to be somewhat more readily oxidized in human liver microsomes, the rates of degradation correlating with increasing lipophilicity. Because the piperidine-2-carboxamide core is chiral, pairs with enantiomeric N-alkyl groups are diastereomeric. While little response to such stereoisomerism was observed for basicity or lipophilicity, more pronounced variations were observed for melting point temperatures and oxidative degradation.