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.

"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.

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.

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.

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.

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.

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.

Fluorination Patterning: A Study of Structural Motifs That Impact Physicochemical Properties of Relevance to Drug Discovery.

The synthesis of a collection of 3-substituted indole derivatives incorporating partially fluorinated n-propyl and n-butyl groups is described along with an in-depth study of the effects of various fluorination patterns on their properties, such as lipophilicity, aqueous solubility, and metabolic stability. The experimental observations confirm predictions of a marked lipophilicity decrease imparted by a vic-difluoro unit when compared to the gem-difluoro counterparts. The data involving the comparison of the two substitution patterns is expected to benefit molecular design in medicinal chemistry and, more broadly, in life as well as materials sciences.

Quantifying the impact of transporters on cellular drug permeability.

The conventional model of drug permeability has recently been challenged. An alternative model proposes that transporter-mediated flux is the sole mechanism of cellular drug permeation, instead of existing in parallel with passive transmembrane diffusion. We examined a central assumption of this alternative hypothesis; namely, that transporters can give rise to experimental observations that would typically be explained with passive transmembrane diffusion. Using systems-biology simulations based on available transporter kinetics and proteomic expression data, we found that such observations are possible in the absence of transmembrane diffusion, but only under very specific conditions that rarely or never occur for known human drug transporters.

Carrier Mediated Distribution System (CAMDIS): a new approach for the measurement of octanol/water distribution coefficients.

Here we present a miniaturized assay, referred to as Carrier-Mediated Distribution System (CAMDIS) for fast and reliable measurement of octanol/water distribution coefficients, log D(oct). By introducing a filter support for octanol, phase separation from water is facilitated and the tendency of emulsion formation (emulsification) at the interface is reduced. A guideline for the best practice of CAMDIS is given, describing a strategy to manage drug adsorption at the filter-supported octanol/buffer interface. We validated the assay on a set of 52 structurally diverse drugs with known shake flask log D(oct) values. Excellent agreement with literature data (r(2) = 0.996, standard error of estimate, SEE = 0.111), high reproducibility (standard deviation, SD < 0.1 log D(oct) units), minimal sample consumption (10 µL of 100 µM DMSO stock solution) and a broad analytical range (log D(oct) range = -0.5 to 4.2) make CAMDIS a valuable tool for the high-throughput assessment of log D(oc)t.

Current approaches and future role of high content imaging in safety sciences and drug discovery.

High content imaging combines automated microscopy with image analysis approaches to simultaneously quantify multiple phenotypic and/or functional parameters in biological systems. The technology has become an important tool in the fields of safety sciences and drug discovery, because it can be used for mode-of-action identification, determination of hazard potency and the discovery of toxicity targets and biomarkers. In contrast to conventional biochemical endpoints, high content imaging provides insight into the spatial distribution and dynamics of responses in biological systems. This allows the identification of signaling pathways underlying cell defense, adaptation, toxicity and death. Therefore, high content imaging is considered a promising technology to address the challenges for the "Toxicity testing in the 21st century" approach. Currently, high content imaging technologies are frequently applied in academia for mechanistic toxicity studies and in pharmaceutical industry for the ranking and selection of lead drug compounds or to identify/confirm mechanisms underlying effects observed in vivo. A recent workshop gathered scientists working on high content imaging in academia, pharmaceutical industry and regulatory bodies with the objective to compile the state-of-the-art of the technology in the different institutions. Together they defined technical and methodological gaps, proposed quality control measures and performance standards, highlighted cell sources and new readouts and discussed future requirements for regulatory implementation. This review summarizes the discussion, proposed solutions and recommendations of the specialists contributing to the workshop.

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.

pKa determination by ¹H NMR spectroscopy - an old methodology revisited.

pKa values of acids and protonated bases have an essential impact on organic synthesis, medicinal chemistry, and material and food sciences. In drug discovery and development, they are of utmost importance for the prediction of pharmacokinetic and pharmacodynamic properties. To date, various methods for the determination of pKa values are available, including UV-spectroscopic, potentiometric, and capillary electrophoretic techniques. An additional option is provided by nuclear magnetic resonance (NMR) spectroscopy. The underlying principle is the alteration of chemical shifts of NMR-active nuclei (e.g., (13)C and (1)H) depending on the protonation state of adjacent acidic or basic sites. When these chemical shifts are plotted against the pH, the inflection point of the resulting sigmoidal curve defines the pKa value. Although pKa determinations by (1)H NMR spectroscopy are reported for numerous cases, the potential of this approach is not yet fully evaluated. We therefore revisited this method with a diverse set of test compounds covering a broad range of pKa values (pKa 0.9-13.8) and made a comparison with four commonly used approaches. The methodology revealed excellent correlations (R(2)=0.99 and 0.97) with electropotentiometric and UV spectroscopic methods. Moreover, the comparison with in silico results (Epik and Marvin) also showed high correlations (R(2)=0.92 and 0.94), further confirming the reliability and utility of this approach.

Physicochemical characterization and in vitro permeation of an indirubin derivative.

The active component of the traditional Chinese medicine, indirubin, exerts anticancer effect on different cancer cell lines. E804, a potent derivative of indirubin inhibits the activation of Stat3 and Stat5 in chronic myelocytic leukaemia (CML) cells. However, physicochemical properties and permeation rate of the compound relevant to the drug formulation have never been reported. Therefore, the ionization constant (pK(a)), lipophilicity (logD/P), aqueous and organic solubility of E804 and its permeation across Caco-2 cells were investigated. Both high throughput and traditional determinations were used in this study. The Caco-2 cell permeation assay was carried out in Poloxamer 188/HBSS++ solution in order to maintain the solubility of drug. The potential P-gp (P-glycoprotein) interaction for E804 was determined through Calcein-AM uptake assay. The results showed that E804 did not have a detectable pK(a) in the range of pH 2-11. Log D (distribution coefficient) and Log P (partition coefficient) were determined to be 3.54 ± 0.03. Aqueous solubility test revealed that E804 is practically insoluble in water. Among organic solvents E804 showed the highest solubility in DMSO. The P(app A→B) and P(app B→A) across Caco-2 cell monolayer were 2.0 ± 0.25 × 10(-6)cm/s and 1.14 ± 0.12 × 10(-6)cm/s respectively, and the calculated efflux ratio (ER) was 0.57. Calcein-AM uptake assay showed that E804 was not a strong substrate for P-gp. The results indicate that solubility is the major rate limiting step for the drug permeation. The high membrane permeability makes E804 promising for the oral delivery. Therefore, further investigation on solubility of E804 in lipid vehicles is needed to determine an appropriate formulation for the drug.

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.

Evidence-based approach to assess passive diffusion and carrier-mediated drug transport.

Evidence supporting the action of passive diffusion and carrier-mediated (CM) transport in drug bioavailability and disposition is discussed to refute the recently proposed theory that drug transport is CM-only and that new transporters will be discovered that possess transport characteristics ascribed to passive diffusion. Misconceptions and faulty speculations are addressed to provide reliable guidance on choosing appropriate tools for drug design and optimization.

Can we discover pharmacological promiscuity early in the drug discovery process?

The term 'pharmacological promiscuity' describes the activity of a single compound against multiple targets. When undesired, promiscuity is a major safety concern that needs to be detected as early as possible in the drug discovery process. The analysis of large datasets reveals that the majority of promiscuous compounds are characterized by recognizable molecular properties and structural motifs, the most important one being a basic center with a pK(a)(B)>6. These compounds interact with a small set of targets such as aminergic GPCRs; some of these targets attract surprisingly high hit rates. In this review, we discuss current trends in the assessment of pharmacological promiscuity and propose strategies to enable early detection and mitigation.

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.