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

Outline and background for the EU-OS solubility prediction challenge

In June 2022, EU-OS came to the decision to make public a solubility data set of 100+K compounds obtained from several of the EU-OS proprietary screening compound collections. Leveraging on the interest of SLAS for screening scientific development it was decided to launch a joint EUOS-SLAS competition within the chemoinformatics and machine learning (ML) communities. The competition was open to real world computation experts, for the best, most predictive, classification model of compound solubility. The aim of the competition was multiple: from a practical side, the winning model should then serve as a cornerstone for future solubility predictions having used the largest training set so far publicly available. From a higher project perspective, the intent was to focus the energies and experiences, even if professionally not precisely coming from Pharma R&D; to address the issue of how to predict compound solubility. Here we report how the competition was ideated and the practical aspects of conducting it within the Kaggle framework, leveraging of the versatility and the open-source nature of this data science platform. Consideration on results and challenges encountered have been also examined.

Deep eutectic solvent self-assembled reverse nanomicelles for transdermal delivery of sparingly soluble drugs

BACKGROUND

Transdermal delivery of sparingly soluble drugs is challenging due to their low solubility and poor permeability. Deep eutectic solvent (DES)/or ionic liquid (IL)-mediated nanocarriers are attracting increasing attention. However, most of them require the addition of auxiliary materials (such as surfactants or organic solvents) to maintain the stability of formulations, which may cause skin irritation and potential toxicity.

RESULTS

We fabricated an amphiphilic DES using natural oxymatrine and lauric acid and constructed a novel self-assembled reverse nanomicelle system (DES-RM) based on the features of this DES. Synthesized DESs showed the broad liquid window and significantly solubilized a series of sparingly soluble drugs, and quantitative structure-activity relationship (QSAR) models with good prediction ability were further built. The experimental and molecular dynamics simulation elucidated that the self-assembly of DES-RM was adjusted by noncovalent intermolecular forces. Choosing triamcinolone acetonide (TA) as a model drug, the skin penetration studies revealed that DES-RM significantly enhanced TA penetration and retention in comparison with their corresponding DES and oil. Furthermore, in vivo animal experiments demonstrated that TA@DES-RM exhibited good anti-psoriasis therapeutic efficacy as well as biocompatibility.

CONCLUSIONS

The present study offers innovative insights into the optimal design of micellar nanodelivery system based on DES combining experiments and computational simulations and provides a promising strategy for developing efficient transdermal delivery systems for sparingly soluble drugs.

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.

Mechanistically transparent models for predicting aqueous solubility of rigid, slightly flexible, and very flexible drugs (MW<2000) Accuracy near that of random forest regression

Yalkowsky's General Solubility Equation (GSE), with its three fixed constants, is popular and easy to apply, but is not very accurate for polar, zwitterionic, or flexible molecules. This review examines the findings of a series of studies, where we have sought to come up with a better prediction model, by comparing the performances of the GSE to Abraham's Solvation Equation (ABSOLV), and Random Forest regression (RFR) machine-learning (ML) method. Large, well-curated aqueous intrinsic solubility databases are available. However, drugs may be sparsely distributed in chemical space, concentrated in clusters. Even a large database might overlook some regions. Test compounds from under-represented portions of space may be poorly predicted, as might be the case with the 'loose' set of 32 drugs in the Second Solubility Challenge (2020). There appears to be still a need for better coverage of drug space. Increasingly, current trends in predictions of solubility use calculated input descriptors, which may be an advantage for exploring properties of molecules yet to be synthesized. The risk may be that overall prediction approaches might be based on accumulated uncertainty. The increasing use of ML/AI methods can lead to accurate predictions, but such predictions may not readily suggest the strategies to pursue in selecting yet-to-be-synthesized compounds. Based on our latest findings, we recommend predictions based on both 'grouped' ABSOLV(GRP) and 'Flexible Acceptor' GSE(Φ,B) models with the provided best-fit parameters, where Φ is the Kier molecular flexibility index and B is the Abraham H-bond acceptor strength. For molecules with Φ < 11, the prudent choice is to pick the Consensus Model, the average of ABSOLV(GRP) and GSE(Φ,B). For more flexible molecules, GSE(Φ,B) is recommended.

Encapsulated polycaprolactone with triazole derivatives and selenium nanoparticles as promising antiproliferative and anticancer agents

Background and purpose

Polycaprolactone nanocapsules incorporated with triazole derivatives in the presence and absence of selenium nanoparticles were prepared and evaluated as antiproliferative and anticancer agents. Polycaprolactone nanoparticles were prepared using the emulsion technique.

Experimental approach

The prepared capsules were characterized using FT-IR, TEM and DLS measurements. The synthesized triazolopyrimidine derivative in the presence and absence of selenium nanoparticles encapsulated in polycaprolactone was tested for its in vitro antiproliferative efficiency towards human breast cancer cell line (MCF7) and murine fibroblast normal cell line (BALB/3T3) in comparison to doxorubicin as a standard anticancer drug.

Key results

The results indicated that encapsulated polycaprolactone with selenium nanoparticles (SeNPs) and triazole-SeNPs were the most potent samples against the tested breast cancer cell line (MCF7). On the other hand, all compounds showed weak or moderate activities towards the tested murine fibroblast normal cell line (BALB/3T3).

Conclusion

As the safety index (SI) was higher than 1.0, it expanded the way for newly synthesized compounds to express antiproliferative efficacy against tumour cells. Hence, these compounds may be considered promising ones. However, they should be examined through further in-vivo and pharmacokinetic studies.

Anomalous salting-out, self-association and pKa effects in the practically-insoluble bromothymol blue

Background and Purpose

The widely-used and practically insoluble diprotic acidic dye, bromothymol blue (BTB), is a neutral molecule in strongly acidic aqueous solutions. The Schill (1964) extensive solubility-pH measurement of bromothymol blue in 0.1 and 1.0 M NaCl solutions, with pH adjusted with HCl from 0.0 to 5.4, featured several unusual findings. The data suggest that the difference in solubility of the neutral-form molecule in 1M NaCl is more than 0.7 log unit lower than the solubility in pure water. This could be considered as uncharacteristically high for a salting-out effect. Also, the study reported two apparent values of pKa1, 1.48 and 1.00, in 0.1 M and 1.0 M NaCl solutions, respectively. The only other measured value found for pKa1 in the literature is -0.66 (Gupta and Cadwallader, 1968).

Experimental Approach

It was reasoned that the there can be only a single pKa1 for BTB. Also, it was hypothesized that salting-out alone might not account for such a large difference in solubility observed at the two levels of salt. A generalized mass action approach incorporating activity corrections for charged species using the Stokes-Robinson hydration equation and for neutral species using the Setschenow equation, was selected to analyze the Schill solubility-pH data to seek a rationalization of these unusual results.

Key Results

BTB reveals complex speciation chemistry in saturated aqueous solutions which had been poorly understood for many years. The appearance of two different values of pKa1 at different levels of NaCl and the anomalously high value of the empirical salting-out constant could be rationalized to normal values by invoking the formation of a very stable neutral dimer (log K2 = 10.0 ± 0.1 M-1). A 'normal' salting-out constant, 0.25 M-1 was then derived. It was also possible to estimate the 'self-interaction' constant. The data analysis in the present study critically depended on the pKa1 = -0.66 reported by Gupta and Cadwallader.

Conclusion

A more reasonable salting-out constant and a consistent single value for pKa1 have been determined by considering a self-interacting (aggregation) model involving an uncharged form of the molecule, which is likely a zwitterion, as suggested by literature spectrophotometric studies.

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset

Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate from the lattice and then, second, be solvated. The melting point of a compound is proportional to the lattice energy, and the octanol-water partition coefficient (log P) is a measure of the compound's solvation efficiency. The CCDC's melting point dataset of almost one hundred thousand compounds was utilized to create widely applicable machine learning models of small molecule melting points. Using the general solubility equation, the aqueous thermodynamic solubilities of the same compounds can be predicted. The global model could be easily localized by adding additional melting point measurements for a chemical series of interest.

Clofazimine pKa Determination by Potentiometry and Spectrophotometry: Reverse Cosolvent Dependence as an Indicator of the Presence of Dimers in Aqueous Solutions

The weakly basic antibiotic and anti-inflammatory drug, clofazimine (CFZ), was first described in 1957. It has been used therapeutically, most notably in the treatment of leprosy. However, the compound is extremely insoluble in aqueous media, and, indeed, there is poor consensus about what its intrinsic solubility is since the reported values range from 0.04 to 11 ng/mL. To understand the speciation and solubilization of CFZ as a function of pH, it is of paramount importance to know the true aqueous pK_a. However, there is also poor consensus about the value of the pK_a (reported measured values range from 6.08 to 9.11). In the present study, we report the determination of the CFZ ionization constant using two independent techniques. A state-of-the-art potentiometric analysis was performed, drawing on titration data in methanol-water solutions (46-75 wt % MeOH) of CFZ, using the bias-reducing consensus of two different procedures of extrapolating the apparent p_sK_a values to zero cosolvent to approximate the true aqueous pK_a as 9.43 ± 0.12 (25 °C, I = 0.15 M reference ionic strength). In parallel, spectrophotometric UV/vis titration data were acquired (250-600 nm at different pH) in 10 mM HEPES buffer solutions containing up to 54 wt % MeOH. The alternating least squares (ALS) method was used in the analysis of the absorbance-pH spectra. Uncharacteristically, the cosolvent UV/vis data in our study showed reverse cosolvent dependence (apparent pK_a values increased with increasing cosolvent) which could be explained by a dimerization of the free base. The analysis of UV/vis data obtained from 54 wt % MeOH-water solution containing 20 μM CFZ yielded the apparent pK_a 9.51 ± 0.17 (I ≈ 0.005 M). To assess whether self-assembly of CFZ was energetically feasible, density functional theory (DFT) calculations were used to study the putative CFZ dimers in aqueous and methanol media. The DFT-optimized geometries and infrared spectra of CFZ dimers using water and methanol as solvents were calculated and analyzed. Based on the lack of negative frequencies in calculated infrared spectra, it was confirmed that optimized geometries correspond to the true energetic minima. Visual analysis of optimized structures indicates the presence of stacking interactions between two CFZ molecules. The protonation site (the imine nitrogen atom) was determined by ¹H NMR spectroscopy.

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.

Recent advances for estimating environmental properties for small molecules from chromatographic measurements and the solvation parameter model

The transfer of neutral compounds between immiscible phases in chromatographic or environmental systems can be described by six solute properties (solute descriptors) using the solvation parameter model. The solute descriptors are size (McGowan's characteristic volume), V, excess molar refraction, E, dipolarity/polarizability, S, hydrogen-bond acidity and basicity, A and B, and the gas-liquid partition constant on n-hexadecane at 298.15 K, L. V and E for liquids are accessible by calculation but the other descriptors and E for solids are determined experimentally by chromatographic, liquid-liquid partition, and solubility measurements. These solute descriptors are available for several thousand compounds in the Abraham solute descriptor databases and for several hundred compounds in the WSU experimental solute descriptor database. In the first part of this review, we highlight features important in defining each descriptor, their experimental determination, compare descriptor quality for the two organized descriptor databases, and methods for estimating Abraham solute descriptors. In the second part we focus on recent applications of the solvation parameter model to characterize environmental systems and its use for the identification of surrogate chromatographic models for estimating environmental properties.

Transformation of Hydrophilic Drug into Oil-Miscible Ionic Liquids for Transdermal Drug Delivery

The transdermal delivery of hydrophilic drugs remains challenging owing to their poor ability to permeate the skin; formulation with oil media is difficult without adding chemical permeation enhancers or co-solvents. Herein, we synthesized 12 oil-miscible ionic liquid (IL) drugs comprising lidocaine-, imipramine-, and levamisole (Lev)-hydrochloride with fatty acid permeation enhancers, i.e., laurate, oleate, linoleate, and stearate as counterions. A set of in vitro and in vivo studies was performed to investigate the potency and deliverability of the transdermal drug formulations. All of the synthesized compounds were freely miscible with pharmaceutically acceptable solvents/agents (i.e., ethanol, N-methyl pyrrolidone, Tween 20, and isopropyl myristate (IPM)). In vitro permeation studies revealed that the oleate-based Lev formulation had 2.6-fold higher skin permeation capability than the Lev salts and also superior ability compared with the laurate-, linoleate-, and stearate-containing samples. Upon in vivo transdermal administration to mice, the peak plasma concentration, elimination half-life, and area under the plasma concentration curve values of Lev-IL were 4.6-, 2.9-, and 5.4-fold higher, respectively, than those of the Lev salt. Furthermore, in vitro skin irritation and in vivo histological studies have demonstrated that Lev-IL has excellent biocompatibility compared with a conventional ionic liquid-based carrier. The results indicate that oil-miscible IL-based drugs provide a simple and scalable strategy for the design of effective transdermal drug delivery systems.