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

Identification of new pharmacophore against SARS-CoV-2 spike protein by multi-fold computational and biochemical techniques

COVID-19 appeared as a highly contagious disease after its outbreak in December 2019 by the virus, named SARS-CoV-2. The threat, which originated in Wuhan, China, swiftly became an international emergency. Among different genomic products, spike protein of virus plays a crucial role in the initiation of the infection by binding to the human lung cells, therefore, SARS-CoV-2's spike protein is a promising therapeutic target. Using a combination of a structure-based virtual screening and biochemical assay, this study seeks possible therapeutic candidates that specifically target the viral spike protein. A database of ~ 850 naturally derived compounds was screened against SARS-CoV-2 spike protein to find natural inhibitors. Using virtual screening and inhibitory experiments, we identified acetyl 11-keto-boswellic acid (AKBA) as a promising molecule for spike protein, which encouraged us to scan the rest of AKBA derivatives in our in-house database via 2D-similarity searching. Later 19 compounds with > 85% similarity with AKBA were selected and docked with receptor binding domain (RBD) of spike protein. Those hits declared significant interactions at the RBD interface, best possess and excellent drug-likeness and pharmacokinetics properties with high gastrointestinal absorption (GIA) without toxicity and allergenicity. Our in-silico observations were eventually validated by in vitro bioassay, interestingly, 10 compounds (A3, A4, C3, C6A, C6B, C6C, C6E, C6H, C6I, and C6J) displayed significant inhibitory ability with good percent inhibition (range: > 72-90). The compounds C3 (90.00%), C6E (91.00%), C6C (87.20%), and C6D (86.23%) demonstrated excellent anti-SARS CoV-2 spike protein activities. The docking interaction of high percent inhibition of inhibitor compounds C3 and C6E was confirmed by MD Simulation. In the molecular dynamics simulation, we observed the stable dynamics of spike protein inhibitor complexes and the influence of inhibitor binding on the protein's conformational arrangements. The binding free energy ΔGTOTAL of C3 (-38.0 ± 0.08 kcal/mol) and C6E (-41.98 ± 0.08 kcal/mol) respectively indicate a strong binding affinity to Spike protein active pocket. These findings demonstrate that these molecules particularly inhibit the function of spike protein and, therefore have the potential to be evaluated as drug candidates against SARS-CoV-2.

A comparison of USP 2 and µDISS Profiler™ apparatus for studying dissolution phenomena of ibuprofen and its salts

BACKGROUND

Pharmaceutical salts of poorly soluble drugs typically dissolve faster than their corresponding free acid or base, resulting in supersaturation under some circumstances. The key questions relevant to drug bioavailability "does the salt invoke the supersaturated state?" and, if so, "does precipitation occur?" remain. To answer these questions, different types of dissolution equipment are often used at different stages of the development process.

AIM

To compare the dissolution behaviour of ibuprofen and its sodium and lysine salts in the USP 2 apparatus and the µDISS Profiler™ apparatus. The dissolution, supersaturation of the salt forms and precipitation to the free acid of ibuprofen were characterized along with the dissolution of the free acid form.

METHODS

Media containing different concentrations of the salt-forming counterions - sodium and lysine - were used to investigate the influence of the type of dissolution apparatus used for the study on dissolution, supersaturation and precipitation behaviour.

KEY RESULTS

Supersaturation was observed for both the sodium and lysinate salts of ibuprofen in all USP 2 apparatus and µDISS Profiler™ experiments. However, precipitation tended to be far greater in the µDISS Profiler™ than in the USP 2 apparatus. The difference was most pronounced in pH 4.5 acetate buffer, in which precipitation was observed exclusively in experiments with the µDISS Profiler™.

CONCLUSION

Choice of dissolution apparatus can affect the dissolution/supersaturation/precipitation characteristics of pharmaceutical salts. This has to be carefully taken into account when investigating salts over different stages of pharmaceutical research and development.

Intrinsic Solubility of Ionizable Compounds from pKa Shift

Aqueous solubility of pharmaceutical substances plays an important role in small molecule drug discovery and development, with ionizable groups often employed to enhance solubility. Drug candidate compounds often contain ionizable groups to increase their solubility. Recognizing that the electrostatically charged form of the compound is much more soluble than the uncharged form, this work proposes a model to explore the relationship between the pKa shift of the ionizable group and dissolution equilibria. The model considers three forms of a compound: dissolved-charged, dissolved-uncharged, and aggregated-uncharged. It analyzes two linked equilibria: the protonation of the ionizable group and the dissolution-aggregation of the uncharged form, with the observed pKa shift depending on the total concentration of the compound. The active concentration of the aggregates determines this shift. The model was explored through the determination of the pKa shift and intrinsic solubility of specific compounds, such as ICPD47, a high-affinity inhibitor of the Hsp90 chaperone protein and anticancer target, as well as benzoic acid and benzydamine. The model holds the potential for a more nuanced understanding of intrinsic solubility and may lead to advancements in drug discovery and development.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

In 1997, the 'Rule of Five' (Ro5) suggested physicochemical limitations for orally administered drugs, based on the analysis of chemical libraries from the early 1990s. In this review, we report on the trends in oral drug product development by analyzing products launched between 1994 and 1997 and between 2013 and 2019. Our analysis confirmed that most new oral drugs are within the Ro5 descriptors; however, the number of new drug products of drugs with molecular weight (MW) and calculated partition coefficient (clogP) beyond the Ro5 has slightly increased. Analysis revealed that there is no single scientific or technological reason for this trend, but that it likely results from incremental advances are being made in molecular biology, target diversity, drug design, medicinal chemistry, predictive modeling, drug metabolism and pharmacokinetics, and drug delivery.

Computational and Experimental Evidence for Templated Macrocyclization: The Role of a Hydrogen Bond Network in the Quantitative Dimerization of 24-Atom Macrocycles

In the absence of preorganization, macrocyclization reactions are often plagued by oligomeric and polymeric side products. Here, a network of hydrogen bonds was identified as the basis for quantitative yields of macrocycles derived from the dimerization of monomers. Oligomers and polymers were not observed. Macrocyclization, the result of the formation of two hydrazones, was hypothesized to proceed in two steps. After condensation to yield the monohydrazone, a network of hydrogen bonds formed to preorganize the terminal acetal and hydrazine groups for cyclization. Experimental evidence for preorganization derived from macrocycles and acyclic models. Solution NMR spectroscopy and single-crystal X-ray diffraction revealed that the macrocycles isolated from the cyclization reaction were protonated twice. These protons contributed to an intramolecular network of hydrogen bonds that engaged distant carbonyl groups to realize a long-range order. DFT calculations showed that this network of hydrogen bonds contributed 8.7 kcal/mol to stability. Acyclic models recapitulated this network in solution. Condensation of an acetal and a triazinyl hydrazine, which adopted a number of conformational isomers, yielded a hydrazone that adopted a favored rotamer conformation in solution. The critical hydrogen-bonded proton was also evident. DFT calculations of acyclic models showed that the rotamers were isoenergetic when deprotonated. Upon protonation, however, energies diverged with one low-energy rotamer adopting the conformation observed in the macrocycle. This conformation anchored the network of hydrogen bonds of the intermediate. Computation revealed that the hydrogen-bonded network in the acyclic intermediate contributed up to 14 kcal/mol of stability and preorganized the acetal and hydrazine for cyclization.

ALipSol: An Attention-Driven Mixture-of-Experts Model for Lipophilicity and Solubility Prediction

Lipophilicity (logD) and aqueous solubility (logS_w) play a central role in drug development. The accurate prediction of these properties remains to be solved due to data scarcity. Current methodologies neglect the intrinsic relationships between physicochemical properties and usually ignore the ionization effects. Here, we propose an attention-driven mixture-of-experts (MoE) model named ALipSol, which explicitly reproduces the hierarchy of task relationships. We adopt the principle of divide-and-conquer by breaking down the complex end point (logD or logS_w) into simpler ones (acidic pK_a, basic pK_a, and logP) and allocating a specific expert network for each subproblem. Subsequently, we implement transfer learning to extract knowledge from related tasks, thus alleviating the dilemma of limited data. Additionally, we substitute the gating network with an attention mechanism to better capture the dynamic task relationships on a per-example basis. We adopt local fine-tuning and consensus prediction to further boost model performance. Extensive evaluation experiments verify the success of the ALipSol model, which achieves RMSE improvement of 8.04%, 2.49%, 8.57%, 12.8%, and 8.60% on the Lipop, ESOL, AqSolDB, external logD, and external logS data sets, respectively, compared with Attentive FP and the state-of-the-art in silico tools. In particular, our model yields more significant advantages (Welch's t-test) for small training data, implying its high robustness and generalizability. The interpretability analysis proves that the atom contributions learned by ALipSol are more reasonable compared with the vanilla Attentive FP, and the substitution effects in benzene derivatives agreed well with empirical constants, revealing the potential of our model to extract useful patterns from data and provide guidance for lead optimization.

Structure Prediction and Potential Inhibitors Docking of Enterovirus 2C Proteins

Human enterovirus infections are mostly asymptomatic and occasionally could be severe and life-threatening. The conserved non-structural 2C from enteroviruses protein is a promising target in antiviral therapies against human enteroviruses. Understanding of 2C-drug interactions is crucial for developing the potential antiviral agents. While functions of enterovirus 2C proteins have been widely studied, three-dimensional structure information of 2C is limited. In this study, the structures of 2C proteins from 20 enteroviruses were simulated and reconstructed using I-TASSER programs. Subsequent docking studies of the known 22 antiviral inhibitors for 2C proteins were performed to uncover the inhibitor-binding characteristics of 2C. Among the potential inhibitors, the compound hydantoin exhibited the highest broad-spectrum antiviral activities with binding to 2C protein. The anti-enteroviral activity of GuaHCL, compound 19b, R523062, compound 12a, compound 12b, quinoline analogs 12a, compound 19d, N⁶-benzyladenosine, dibucaine derivatives 6i, TBZE-029, fluoxetine analogs 2b, dibucaine, 2-(α-hydroxybenzyl)-benzimidazole (HBB), metrifudil, pirlindole, MRL-1237, quinoline analogs 10a, zuclopenthixol, fluoxetine, fluoxetine HCl, and quinoline analogs 12c showed a trend of gradual decrease. In addition, the free energy with 22 compounds binding to EV 2C ranged from −0.35 to −88.18 kcal/mol. Our in silico studies will provide important information for the development of pan-enterovirus antiviral agents based on 2C.

Designing Soluble PROTACs: Strategies and Preliminary Guidelines

Solubility optimization is a crucial step to obtaining oral PROTACs. Here we measured the thermodynamic solubilities (log S) of 21 commercial PROTACs. Next, we measured BRlogD and log k_w^IAM (lipophilicity), EPSA, and Δ log k_w^IAM (polarity) and showed that lipophilicity plays a major role in governing log S, but a contribution of polarity cannot be neglected. Two-/three-dimensional descriptors calculated on conformers arising from conformational sampling and steered molecular dynamics failed in modeling solubility. Infographic tools were used to identify a privileged region of soluble PROTACs in a chemical space defined by BRlogD, log k_w^IAM and topological polar surface area, while machine learning provided a log S classification model. Finally, for three pairs of PROTACs we measured the solubility, lipophilicity, and polarity of the building blocks and identified the limits of estimating PROTAC solubility from the synthetic components. Overall, this paper provides promising guidelines for optimizing PROTAC solubility in early drug discovery programs.

New therapeutic modalities in drug discovery and development: Insights & opportunities

New Therapeutic Modalities in Drug Discovery and Development: Insights & Opportunities (Editorial for the special issue of ADMET and DMPK)

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, log₁₀S₀, 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 S₀ 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-pS₀ database (nearly 400 added log₁₀ S₀ 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.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

Molecular photoswitches in aqueous environments

Molecular photoswitches enable dynamic control of processes with high spatiotemporal precision, using light as external stimulus, and hence are ideal tools for different research areas spanning from chemical biology to smart materials. Photoswitches are typically organic molecules that feature extended aromatic systems to make them responsive to (visible) light. However, this renders them inherently lipophilic, while water-solubility is of crucial importance to apply photoswitchable organic molecules in biological systems, like in the rapidly emerging field of photopharmacology. Several strategies for solubilizing organic molecules in water are known, but there are not yet clear rules for applying them to photoswitchable molecules. Importantly, rendering photoswitches water-soluble has a serious impact on both their photophysical and biological properties, which must be taken into consideration when designing new systems. Altogether, these aspects pose considerable challenges for successfully applying molecular photoswitches in aqueous systems, and in particular in biologically relevant media. In this review, we focus on fully water-soluble photoswitches, such as those used in biological environments, in both in vitro and in vivo studies. We discuss the design principles and prospects for water-soluble photoswitches to inspire and enable their future applications.

PharmaNet: Pharmaceutical discovery with deep recurrent neural networks

The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 97.7% in the Receiver Operating Characteristic curve (ROC-AUC) and 65.5% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained three (3) potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.

Steering New Drug Discovery Campaigns: Permeability, Solubility, and Physicochemical Properties in the bRo5 Chemical Space

An increasing number of drug discovery programs concern compounds in the beyond rule of 5 (bRo5) chemical space, such as cyclic peptides, macrocycles, and degraders. Recent results show that common paradigms of property-based drug design need revision to be applied to larger and more flexible compounds. A virtual event entitled "Solubility, permeability and physico-chemical properties in the bRo5 chemical space" was organized to provide preliminary guidance on how to make the discovery of oral drugs in the bRo5 space more effective. The four speakers emphasized the importance of the bRo5 space as a source of new oral drugs and provided examples of experimental and computational methods specifically tailored for design and optimization in this chemical space.

Synergistic Computational Modeling Approaches as Team Players in the Game of Solubility Predictions

Several approaches to predict and model drug solubility have been used in the drug discovery and development processes during the last decades. Each of these approaches have their own benefits and place, and are typically used as standalone approaches rather than in concert. The synergistic effects of these are often overlooked, partly due to the need of computational experts to perform the modeling and simulations as well as analyzing the data obtained. Here we provide our views on how these different approaches can be used to retrieve more information on drug solubility, ranging from multivariate data analysis over thermodynamic cycle modeling to molecular dynamics simulations. We are discussing aqueous solubility as well as solubility in more complex mixed solvents and media with colloidal structures present. We conclude that the field of computational pharmaceutics is in its early days but with a bright future ahead. However, education of computational formulators with broad knowledge of modeling and simulation approaches is imperative if computational pharmaceutics is to reach its full potential.