High throughput solubility measurement in drug discovery and development

Biopharmaceutical profiling of anti-infective sanggenons from Morus alba root bark for inhalation administration

Mulberry Diels-Alder-type adducts (MDAAs), isolated from Morus alba root bark, exhibit dual activity against viral and bacterial pathogens but show sobering efficacy following oral administration. Inhalation administration may overcome issues with oral bioavailability and improve efficacy for the treatment of respiratory infections. To assess the suitability of MDAAs for inhalation administration, physicochemical (e.g. pH, pKa, logP, pH-dependent solubility) and biopharmaceutical (epithelial cytotoxicity, permeability, and uptake) properties of two bioactive MDAA stereoisomers sanggenon C (SGC) and sanggenon D (SGD) were evaluated as isolated natural compounds and within parent extracts (MA21, MA60). Despite their structural similarity, SGD exhibited a 10-fold higher solubility than SGC across pH 1.2-7.4, with slight increases at neutral pH. Both compounds were more soluble in isolated form than in the parent extracts. The more lipophilic SGC was found to be more cytotoxic when compared to SGD, indicating a better cellular penetration, which was confirmed by uptake studies. Nonetheless, SGC and SGD exhibited no measurable permeability across intact Calu-3 monolayers, highlighting their potential for increased lung retention and improved local anti-infective activity following inhalation administration. Results suggest that SGC and SGD in isolated form, rather than as extracts, are promising candidates for pulmonary drug delivery to treat lung infections.

Rhoifolin, baicalein 5,6-dimethyl ether and agathisflavone prevent amnesia induced in scopolamine zebrafish (Danio rerio) model by increasing the mRNA expression of bdnf, npy, egr-1, nfr2α, and creb1 genes

The increasing attention towards age-related diseases has generated significant interest in the concept of cognitive dysfunction associated with Alzheimer's disease (AD). Certain limitations are associated with the current therapies, and flavonoids have been reported to exhibit multiple biological activities and anti-AD effects in several AD models owing to their antioxidative, anti-inflammatory, and anti-amyloidogenic properties. In this study, we performed an initial in silico predictions of the pharmacokinetic properties of three flavonoids (rhoifolin, baicalein 5,6-dimethyl ether and agathisflavone). Subsequently, we evaluated the antiamnesic and antioxidant potential of flavonoids in concentrations of 1, 3, and 5 μg/L in scopolamine (100 μM)-induced amnesic zebrafish (Danio rerio) model. Zebrafish behavior was analyzed by novel tank diving test (NTT), Y-maze, and novel object recognition test (NOR). Acetylcholinesterase (AChE) activity, brain antioxidant status and the expression of bdnf, npy, egr1, nrf2α, creb1 genes, and CREB-1 protein level was measured to elucidate the underlying mechanism of action. Our flavonoids improved memory and decreased anxiety-like behavior of scopolamine-induced amnesia in zebrafish. Also, the studied flavonoids reduced AChE activity and brain oxidative stress and upregulated the gene expression, collectively contributing to neuroprotective properties. The results of our study add new perspectives on the properties of flavonoids to regulate the evolution of neurodegenerative diseases, especially AD, by modulating the expression of genes involved in the regulation of synaptic plasticity, axonal growth, and guidance, sympathetic and vagal transmission, the antioxidant response and cell proliferation and growth.

Application of shadowgraph imaging (SGI) particle characterisation data to interpret the impact of varying test conditions on power dissolution and to develop an automated agglomeration identification method (AIM) in the USP flow through apparatus

The aims of this work were 1) to explore the application of shadowgraph imaging (SGI) as a real time monitoring tool to characterize ibuprofen particle behaviour during dissolution testing under various conditions in the USP 4 flow-through apparatus and 2) to investigate the potential to develop an SGI-based automated agglomeration identification method (AIM) for real time agglomerate detection during dissolution testing. The effect of surfactant addition, changes in the drug mass and flow rate, the use of sieved and un-sieved powder fractions, and the use of different drug crystal habits were investigated. Videos at every sampling time point during dissolution were taken and analysed by SGI. The AIM was developed to characterize agglomerates based on two criteria - size and solidity. All detections were confirmed by manual video observation and a reference agglomerate data set. The method was validated under new dissolution conditions with un-sieved particles. Characterisation of particle dispersion behaviour by SGI enabled interpretation of the impact of dissolution test conditions. Higher numbers of early detections reflected greater dissolution rates with increased surfactant concentration, using sieved fraction or plate-shaped crystals, but was impacted by drug mass tested. An AIM was successfully developed and applied to detect agglomerates during dissolution, suggesting potential, with appropriate method development, for application in quality control.

Development and Validation of an LC-MS/MS Assay for the Quantitation of MO-OH-Nap Tropolone in Mouse Plasma: Application to In Vitro and In Vivo Pharmacokinetic Studies

A rapid, selective, and sensitive liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) method was developed and validated for the quantitation of MO-OH-Nap tropolone (MO-OH-Nap) in mouse plasma. MO-OH-Nap is an α-substituted tropolone with anti-proliferative properties in various cancer cell lines. Detection and separation of analytes was achieved on an ACE Excel C18 (1.7 µm, 100 × 2.1 mm, MAC-MOD Analytical, Chadds Ford, PA, USA) column with mobile phase consisting of 0.05% trifluoroacetic acid in water (mobile phase A) and 0.05% trifluoroacetic acid in acetonitrile (mobile phase B), with an isocratic elution of 15:85% (A:B) at a total flow rate of 0.25 mL/min. The LC-MS/MS system was operated at unit resolution in multiple reaction monitoring (MRM) mode, using precursor ion > product ion combination of 249.10 > 202.15 m/z for MO-OH-Nap and 305.10 > 215.05 m/z for the internal standard (IS), BA-SM-OM. The MS/MS response was linear over a concentration range of 1 to 500 ng/mL with a correlation coefficient (r2) of ≥0.987. The within- and between-batch precision (%RSD) and accuracy (%Bias) were within acceptable limits. The validated method was successfully applied to determine MO-OH-Nap metabolic stability, plasma protein binding, and bio-distribution studies of MO-OH-Nap in CD-1 mice.

Amorphous Solubility Advantage: Theoretical Considerations, Experimental Methods, and Contemporary Relevance

Twenty-five years ago, Hancock and Parks asked a provocative question: "what is the true solubility advantage for amorphous pharmaceuticals?" Difficulties in determining the amorphous solubility have since been overcome due to significant advances in theoretical understanding and experimental methods. The amorphous solubility is now understood to be the concentration after the drug undergoes liquid-liquid or liquid-glass phase separation, forming a water-saturated drug-rich phase in metastable equilibrium with an aqueous phase containing molecularly dissolved drug. While crystalline solubility is an essential parameter impacting the absorption of crystalline drug formulations, amorphous solubility is a vital factor for considering absorption from supersaturating formulations. However, the amorphous solubility of drugs is complex, especially in the presence of formulation additives and gastrointestinal components, and concentration-based measurements may not indicate the maximum drug thermodynamic activity. This review discusses the concept of the amorphous solubility advantage, including a historical perspective, theoretical considerations, experimental methods for amorphous solubility measurement, and the contribution of supersaturation and amorphous solubility to drug absorption. Leveraging amorphous solubility and understanding the associated physicochemical principles can lead to more effective development strategies for poorly water-soluble drugs, ultimately benefiting therapeutic outcomes.

Discovery of the DNA-PKcs inhibitor DA-143 which exhibits enhanced solubility relative to NU7441

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) plays a vital role in DNA damage repair and lymphocyte function, presenting a significant target in cancer and immune diseases. Current DNA-PKcs inhibitors are undergoing Phase I/II trials as adjuncts to radiotherapy and chemotherapy in cancer. Nevertheless, clinical utility is limited by suboptimal bioavailability. This study introduces DNA-PKcs inhibitors designed to enhance bioavailability. We demonstrate that a novel DNA-PKcs inhibitor, DA-143, surpasses NU7441 in aqueous solubility as well as other available inhibitors. In addition, DA-143 displayed an improvement in DNA-PKcs inhibition relative to NU7441 achieving an IC50 of 2.5 nM. Consistent with current inhibitors, inhibition of DNA-PKcs by DA-143 resulted in increased tumor cell sensitivity to DNA-damage from chemotherapy and inhibition of human T cell function. The improved solubility of DA-143 is critical for enhanced efficacy at reduced doses and facilitates more effective evaluation of DNA-PKcs inhibition in both preclinical and clinical development.

Rational In Silico Design of Selective TMPRSS6 Peptidomimetic Inhibitors via Exploitation of the S2 Subpocket

TMPRSS6 is a potential therapeutic target for the treatment of iron overload due to its role in regulating levels of hepcidin. Although potent TMPRSS6 inhibitors have been previously developed, their lack of specificity requires optimization to avoid potential side effects before pursuing preclinical development with in vivo models. Here, using computer-aided drug design based on a TMPRSS6 homology model, we reveal that the S2 position of TMPRSS6 offers a potential avenue to achieve selectivity against other members of the TTSP family. Accordingly, we synthesized novel peptidomimetic molecules containing lipophilic amino acids at the P2 position to exploit this unexplored pocket. This enabled us to identify TMPRSS6-selective small molecules with low nanomolar affinity. Finally, pharmacokinetic parameters were determined, and a compound was found to be potent in cellulo toward its primary target while retaining TTSP-subtype selectivity and showing no signs of alteration in in vitro TEER experiments.

From mundane to surprising nonadditivity: drivers and impact on ML models

Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural assignment. While purely ligand-based conformational causes of NA are rather well understood and mundane, other factors are less so and cause surprising NA that has a huge influence on SAR analysis and ML model performance. We here report a systematic analysis across a wide range of properties (20 on-target biological activities and 4 physicochemical ADME-related properties) to understand the frequency of various different phenomena that may lead to NA. A set of novel descriptors were developed to characterize double transformation cycles and identify trends in NA. Double transformation cycles were classified into "surprising" and "mundane" categories, with the majority being classed as mundane. We also examined commonalities among surprising cycles, finding LogP differences to have the most significant impact on NA. A distinct behavior of NA for on-target sets compared to ADME sets was observed. Finally, we show that machine learning models struggle with highly nonadditive data, indicating that a better understanding of NA is an important future research direction.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

High-throughput nephelometry methodology for qualitative determination of aqueous solubility of chemical libraries

The purpose of the protocol reported in this work is the solubility profiling of large chemical libraries using nephelometry. This technique allows the qualitative classification of compounds as highly, moderately, or poorly water-soluble. The described methodology is not intended to yield quantitative solubility values of the studied compounds but can be used as a primary solubility assessment of large chemical libraries, to guide hit prioritization after High Throughput Screening (HTS) campaigns.

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

Preparation and characterization of new salts of tioconazole. Comparison of their dissolution performance

Tioconazole is an effective antifungal agent with very low solubility in aqueous media, which limits its bioavailability and efficacy. Aiming to overcome the drug limitations by improving the solubility of this active pharmaceutical ingredient, solution precipitation techniques were employed to prepare four new crystalline salts, namely the mesylate, tosylate, maleate (1:1), and fumarate (1:1) hemihydrate. The thermal stabilities, dissolution properties, and structural characteristics of the solids were determined, and the study was extended to compare their properties with the already-known oxalate salt. The structural characterization of the new phases was carried out using a multi-method approach, which included thermal (differential scanning calorimetry and thermogravimetry), diffractometric (powder X-ray diffraction), and spectroscopic (near-infrared and mid-infrared) methodologies. The determination of the melting point of the salts confirmed the findings made by thermal methods. Functional characteristics of the salts, involving their intrinsic dissolution rates were also determined. It was found that the salts exhibited improved thermal stability and that the nature of the counterion modulated their dissolution characteristics. The salts displayed better intrinsic dissolution rates than the free base, to the point of being "highly soluble" according to the Biopharmaceutical Classification System. At pH 4.3, the sulfonic acid derivatives exhibited better dissolution rates than their carboxylic acid-derived counterparts, greatly improved regarding bare tioconazole. The results suggest that the salts have great potential to be used as replacements for the free base; in principle, careful salt selection may help to fulfill each solubility need for the different scenarios where the drug may be used.

The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge

The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, medium and highly soluble compounds as measured by the nephelometry assay. This article describes the winning model, which was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM) available on the website https://ochem.eu/article/27. We describe in detail the assumptions and steps used to select methods, descriptors and strategy which contributed to the winning solution. In particular we show that consensus based on 28 models calculated using descriptor-based and representation learning methods allowed us to obtain the best score, which was higher than those based on individual approaches or consensus models developed using each individual approach. A combination of diverse models allowed us to decrease both bias and variance of individual models and to calculate the highest score. The model based on Transformer CNN contributed the best individual score thus highlighting the power of Natural Language Processing (NLP) methods. The inclusion of information about aleatoric uncertainty would be important to better understand and use the challenge data by the contestants.

Discovery of furopyridine-based compounds as novel inhibitors of Janus kinase 2: In silico and in vitro studies

Janus kinase 2 (JAK2), one of the JAK isoforms participating in a JAK/STAT signaling cascade, has been considered a potential clinical target owing to its critical role in physiological processes involved in cell growth, survival, development, and differentiation of various cell types, especially immune and hematopoietic cells. Substantial studies have proven that the inhibition of this target could disrupt the JAK/STAT pathway and provide therapeutic outcomes for cancer, immune disorders, inflammation, and COVID-19. Herein, we performed docking-based virtual screening of 63 in-house furopyridine-based compounds and verified the first-round screened compounds by in vitro enzyme- and cell-based assays. By shedding light on the integration of both in silico and in vitro methods, we could elucidate two promising compounds. PD19 showed cytotoxic effects on human erythroblast cell lines (TF-1 and HEL) with IC50 values of 57.27 and 27.28 μM, respectively, while PD12 exhibited a cytotoxic effect on TF-1 with an IC50 value of 83.47 μM by suppressing JAK2/STAT5 autophosphorylation. In addition, all screened compounds were predicted to meet drug-like criteria based on Lipinski's rule of five, and none of the extreme toxicity features were found. Molecular dynamic simulations revealed that PD12 and PD19 could form stable complexes with JAK2 in an aqueous environment, and the van der Waals interactions were the main force driving the complex formation. Besides, all compounds sufficiently interacted with surrounding amino acids in all crucial regions, including glycine, catalytic, and activation loops. Altogether, PD12 and PD19 identified here could potentially be developed as novel therapeutic inhibitors disrupting the JAK/STAT pathway.

Kinetic solubility: Experimental and machine-learning modeling perspectives

Kinetic aqueous or buffer solubility is important parameter measuring suitability of compounds for high throughput assays in early drug discovery while thermodynamic solubility is reserved for later stages of drug discovery and development. Kinetic solubility is also considered to have low inter-laboratory reproducibility because of its sensitivity to protocol parameters [1]. Presumably, this is why little efforts have been put to build QSPR models for kinetic in comparison to thermodynamic aqueous solubility. Here, we investigate the reproducibility and modelability of kinetic solubility assays. We first analyzed the relationship between kinetic and thermodynamic solubility data, and then examined the consistency of data from different kinetic assays. In this contribution, we report differences between kinetic and thermodynamic solubility data that are consistent with those reported by others [1, 2] and good agreement between data from different kinetic solubility campaigns in contrast to general expectations. The latter is confirmed by achieving high performing QSPR models trained on merged kinetic solubility datasets. The poor performance of QSPR model trained on thermodynamic solubility when applied to kinetic solubility dataset reinforces the conclusion that kinetic and thermodynamic solubilities do not correlate: one cannot be used as an ersatz for the other. This encourages for building predictive models for kinetic solubility. The kinetic solubility QSPR model developed in this study is freely accessible through the Predictor web service of the Laboratory of Chemoinformatics (https://chematlas.chimie.unistra.fr/cgi-bin/predictor2.cgi).

Discovery of Orally Available and Brain Penetrant AEP Inhibitors

Alzheimer's Disease (AD) is the most widespread form of dementia, with one of the pathological hallmarks being the formation of neurofibrillary tangles (NFTs). These tangles consist of phosphorylated Tau fragments. Asparagine endopeptidase (AEP) is a key Tau cleaving enzyme that generates aggregation-prone Tau fragments. Inhibition of AEP to reduce the level of toxic Tau fragment formation could represent a promising therapeutic strategy. Here, we report the first orthosteric, selective, orally bioavailable, and brain penetrant inhibitors with an irreversible binding mode. We outline the development of the series starting from reversible molecules and demonstrate the link between inhibition of AEP and reduction of Tau N368 fragment both in vitro and in vivo.

In vitro and in silico methods to investigate the effect of moderately increasing medium viscosity and density on ibuprofen dissolution rate

Medium viscosity can affect drug dissolution rate, however, it is not usually considered in routine dissolution testing or less complex biorelevant media. The effects of moderately increasing medium viscosity on the in vitro and in silico dissolution of ibuprofen were investigated with two viscosity enhancing agents (VEA) (hydroxypropyl methylcellulose (HPMC) and sucrose), three viscosity levels (range 0.7-5.5 mPa.s), two solubilities and two fluid velocities in the paddle, flow-through and intrinsic dissolution apparatuses. A factorial design analysis highlighted which factors significantly affected key dissolution metrics. Experimental results in the flow-through apparatus (FTA) were compared with in silico dissolution profiles generated by an in-house simulation code (SIMDISSOTM). Increasing viscosity reduced the intrinsic dissolution rate of ibuprofen for both VEAs. The dissolution rate reduction was also observed in the FTA with sucrose, but less so with HPMC, suggesting particle wetting, motion and surface area effects. Particle motion simulations suggested reduced particle lifting times as viscosity increased, indicating an effect of viscosity on particle dispersal. The viscosity- and fluid density-mediated reduction in the dissolution rate observed with sucrose was accurately simulated by SIMDISSOTM, in particular at higher velocities. Velocity had a significant impact on dissolution rates in the paddle apparatus, with a significant viscosity-related reduction in dissolution observed in the low solubility-low velocity scenario. Even small increases in medium viscosity can reduce the dissolution rate of a BCS class II drug, and in silico particle motion and dissolution data can assist interpretation of particulate dissolution behaviour.

Investigation of new ferrocenyl-artesunate derivatives as antiparasitics

Artesunate (Ars) is a semisynthetic antimalarial drug and is a part of the artemisinin-based combination therapy arsenal employed for malaria treatment. The drug functions mainly by activation of its endoperoxide bridge leading to increased oxidative stress in malaria parasites. The purpose of this study was to ascertain the antiparasitic effects of combining ferrocene and Arsvia short or long chain ester or amide linkages (C1-C4). The compounds were evaluated for growth inhibition activity on the apicomplexan parasites, Plasmodium falciparum (P. falciparum) and Toxoplasma gondii (T. gondii). All the complexes demonstrated good activity against T. gondii with IC50 values in the low micromolar range (0.28-1.2 μM) and good to excellent antimalarial activity against a chloroquine sensitive strain of P. falciparum (NF54). Further investigations on T. gondii revealed that the likely mode of action (MoA) is through the generation of reactive oxygen species. Additionally, immunofluorescence microscopy suggested a novel change in the morphology of the parasite by complex C3, an artesunate-ferrocenyl ethyl amide complex. The complexes were not cytotoxic or showed low cytotoxicity to two normal cell lines tested.

Solubility and Thermodynamic Analysis of Isotretinoin in Different (DMSO + Water) Mixtures

The solubility and solution thermodynamics of isotretinoin (ITN) (3) in numerous {dimethyl sulfoxide (DMSO) (1) + water (H2O) (2)} combinations were studied at 298.2-318.2 K under fixed atmospheric pressure of 101.1 kPa. A shake flask methodology was used to determine ITN solubility, and correlations were made using the "van't Hoff, Apelblat, Buchowski-Ksiazczak λh, Yalkowsky-Roseman, Jouyban-Acree, and Jouyban-Acree-van't Hoff models". In mixtures of {(DMSO (1) + H2O (2)}, the solubility of ITN in mole fractions was enhanced with the temperature and DMSO mass fraction. The mole fraction solubility of ITN was highest in neat DMSO (1.02 × 10-1 at 318.2 K) and lowest in pure H2O (3.14 × 10-7 at 298.2 K). The output of computational models revealed good relationships between the solubility data from the experiments. The dissolution of ITN was "endothermic and entropy-driven" in all of the {(DMSO (1) + H2O (2)} mixtures examined, according to the positive values of measured thermodynamic parameters. Enthalpy was discovered to be the driving force behind ITN solvation in {(DMSO (1) + H2O (2)} combinations. ITN-DMSO displayed the highest molecular interactions when compared to ITN-H2O. The outcomes of this study suggest that DMSO has a great potential for solubilizing ITN in H2O.

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.

pH-dependent solubility prediction for optimized drug absorption and compound uptake by plants

Aqueous solubility is the most important physicochemical property for agrochemical and drug candidates and a prerequisite for uptake, distribution, transport, and finally the bioavailability in living species. We here present the first-ever direct machine learning models for pH-dependent solubility in water. For this, we combined almost 300000 data points from 11 solubility assays performed over 24 years and over one million data points from lipophilicity and melting point experiments. Data were split into three pH-classes - acidic, neutral and basic - , representing the conditions of stomach and intestinal tract for animals and humans, and phloem and xylem for plants. We find that multi-task neural networks using ECFP-6 fingerprints outperform baseline random forests and single-task neural networks on the individual tasks. Our final model with three solubility tasks using the pH-class combined data from different assays and five helper tasks results in root mean square errors of 0.56 log units overall (acidic 0.61; neutral 0.52; basic 0.54) and Spearman rank correlations of 0.83 (acidic 0.78; neutral 0.86; basic 0.86), making it a valuable tool for profiling of compounds in pharmaceutical and agrochemical research. The model allows for the prediction of compound pH profiles with mean and median RMSE per molecule of 0.62 and 0.56 log units.

Computational Predictions of Nonclinical Pharmacokinetics at the Drug Design Stage

Although computational predictions of pharmacokinetics (PK) are desirable at the drug design stage, existing approaches are often limited by prediction accuracy and human interpretability. Using a discovery data set of mouse and rat PK studies at Roche (9,685 unique compounds), we performed a proof-of-concept study to predict key PK properties from chemical structure alone, including plasma clearance (CLp), volume of distribution at steady-state (Vss), and oral bioavailability (F). Ten machine learning (ML) models were evaluated, including Single-Task, Multitask, and transfer learning approaches (i.e., pretraining with in vitro data). In addition to prediction accuracy, we emphasized human interpretability of outcomes, especially the quantification of uncertainty, applicability domains, and explanations of predictions in terms of molecular features. Results show that intravenous (IV) PK properties (CLp and Vss) can be predicted with good precision (average absolute fold error, AAFE of 1.96-2.84 depending on data split) and low bias (average fold error, AFE of 0.98-1.36), with AutoGluon, Gaussian Process Regressor (GP), and ChemProp displaying the best performance. Driven by higher complexity of oral PK studies, predictions of F were more challenging, with the best AAFE values of 2.35-2.60 and higher overprediction bias (AFE of 1.45-1.62). Multi-Task approaches and pretraining of ChemProp neural networks with in vitro data showed similar precision to Single-Task models but helped reduce the bias and increase correlations between observations and predictions. A combination of GP-computed prediction variance, molecular clustering, and dimensionality-reduction provided valuable quantitative insights into prediction uncertainty and applicability domains. SHAPley Additive exPlanations (SHAPs) highlighted molecular features contributing to prediction outcomes of Vss, providing explanations that could aid drug design. Combined results show that computational predictions of PK are feasible at the drug design stage, with several ML technologies converging to successfully leverage historical PK data sets. Further studies are needed to unlock the full potential of this approach, especially with respect to data set sizes and quality, transfer learning between in vitro and in vivo data sets, model-independent quantification of uncertainty, and explainability of predictions.

Halide Perovskite Crystallization Processes and Methods in Nanocrystals, Single Crystals, and Thin Films

Halide perovskite semiconductors with extraordinary optoelectronic properties have been fascinatedly studied. Halide perovskite nanocrystals, single crystals, and thin films have been prepared for various fields, such as light emission, light detection, and light harvesting. High-performance devices rely on high crystal quality determined by the nucleation and crystal growth process. Here, the fundamental understanding of the crystallization process driven by supersaturation of the solution is discussed and the methods for halide perovskite crystals are summarized. Supersaturation determines the proportion and the average Gibbs free energy changes for surface and volume molecular units involved in the spontaneous aggregation, which could be stable in the solution and induce homogeneous nucleation only when the solution exceeds a required minimum critical concentration (C_min ). Crystal growth and heterogeneous nucleation are thermodynamically easier than homogeneous nucleation due to the existent surfaces. Nanocrystals are mainly prepared via the nucleation-dominated process by rapidly increasing the concentration over C_min , single crystals are mainly prepared via the growth-dominated process by keeping the concentration between solubility and C_min , while thin films are mainly prepared by compromising the nucleation and growth processes to ensure compactness and grain sizes. Typical strategies for preparing these three forms of halide perovskites are also reviewed.

Autoantibodies and Cardiomyopathy: Focus on Beta-1 Adrenergic Receptor Autoantibodies

ABSTRACT

Antibody response to self-antigens leads to autoimmune response that plays a determinant role in cardiovascular disease outcomes including dilated cardiomyopathy (DCM). Although the origins of the self-reactive endogenous autoantibodies are not well-characterized, it is believed to be triggered by tissue injury or dysregulated humoral response. Autoantibodies that recognize G protein-coupled receptors are considered consequential because they act as modulators of downstream receptor signaling displaying a wide range of unique pharmacological properties. These wide range of pharmacological properties exhibited by autoantibodies has cellular consequences that is associated with progression of disease including DCM. Increase in autoantibodies recognizing beta-1 adrenergic receptor (β1AR), a G protein-coupled receptor critical for cardiac function, is observed in patients with DCM. Cellular and animal model studies have indicated pathological roles for the β1AR autoantibodies but less is understood about the molecular basis of their modulatory effects. Despite the recognition that β1AR autoantibodies could mediate deleterious outcomes, emerging evidence suggests that not all β1AR autoantibodies are deleterious. Recent clinical studies show that β1AR autoantibodies belonging to the IgG3 subclass is associated with beneficial cardiac outcomes in patients. This suggests that our understanding on the roles the β1AR autoantibodies play in mediating outcomes is not well-understood. Technological advances including structural determinants of antibody binding could provide insights on the modulatory capabilities of β1AR autoantibodies in turn, reflecting their diversity in mediating β1AR signaling response. In this study, we discuss the significance of the diversity in signaling and its implications in pathology.

The Rationale for "Laser-Induced Thermal Therapy (LITT) and Intratumoral Cisplatin" Approach for Cancer Treatment

Cisplatin is one of the most widely used anticancer drugs in the treatment of various types of solid human cancers, as well as germ cell tumors, sarcomas, and lymphomas. Strong evidence from research has demonstrated higher efficacy of a combination of cisplatin and derivatives, together with hyperthermia and light, in overcoming drug resistance and improving tumoricidal efficacy. It is well known that the antioncogenic potential of CDDP is markedly enhanced by hyperthermia compared to drug treatment alone. However, more recently, accelerators of high energy particles, such as synchrotrons, have been used to produce powerful and monochromatizable radiation to induce an Auger electron cascade in cis-platinum molecules. This is the concept that makes photoactivation of cis-platinum theoretically possible. Both heat and light increase cisplatin anticancer activity via multiple mechanisms, generating DNA lesions by interacting with purine bases in DNA followed by activation of several signal transduction pathways which finally lead to apoptosis. For the past twenty-seven years, our group has developed infrared photo-thermal activation of cisplatin for cancer treatment from bench to bedside. The future development of photoactivatable prodrugs of platinum-based agents injected intratumorally will increase selectivity, lower toxicity and increase efficacy of this important class of antitumor drugs, particularly when treating tumors accessible to laser-based fiber-optic devices, as in head and neck cancer. In this article, the mechanistic rationale of combined intratumor injections of cisplatin and laser-induced thermal therapy (CDDP-LITT) and the clinical application of such minimally invasive treatment for cancer are reviewed.

Identification of 1,2,4-Oxadiazoles-Based Novel EGFR Inhibitors: Molecular Dynamics Simulation-Guided Identification and in vitro ADME Studies

Background

In this work, we have identified heterocyclic derivatives with 1,2,4 oxadiazole scaffold mimicking the functions of tyrosine kinase inhibitors. Fourteen molecules that displayed the best fit were picked from the library of compounds and studied under in-silico and in-vitro conditions. Four compounds were selected for further cytotoxicity and ADME (Absorption, Distribution, Metabolism, Elimination) profiling showing IC₅₀ (from 8–13 µM) values against EGFR positive cancer cell line (MCF7).

Methods

A molecular dynamics simulation study was performed to understand the correlation of non-covalent binding energies with biological activity. The drug-like properties of the selected four compounds (7a, 7b, 7e, and 7m) were evaluated by in-vitro ADME studies. Compounds 7a, 7b, and 7m were the active compounds in the molecular dynamics simulations study. Further, EGFR binding activity was confirmed with EGFR^WT and EGFR^T790M kinase assay using a luminescence-based method.

Results

These compounds (7a, 7b, and 7m) showed activity against EGFR^WT and mutant EGFR^T790M, exhibiting IC₅₀ values of <10 and <50 micromolar, respectively. These compounds also possess moderate aqueous solubility in 40–70 µg/mL at pH 7.4 and 30–100 µg/mL at pH 4.0. Further, 7a, 7b, and 7m showed balanced lipophilicity with Log D values ranging from 1–3. They demonstrated a good correlation in Caco-2 permeability with Apparent permeability (Papp) 1 to 5 × 10⁻⁶ cm/s in comparison with 7e, which was found to be highly lipophilic (Log D >5) and showed high permeability (Papp 17 × 10⁻⁶ cm/s). Lastly, all these compounds were moderately stable in liver microsomes at alkaline pH with a half-life of 30–60 min, while at a highly acidic pH (2.0), the compounds were stable up to 15–20 min.

Conclusion

Overall, in-vitro ADME results of these molecules showed good drug-like properties, which are well correlated with the in-silico ADME data, making them ideal for developing an oral drug delivery formulation.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up” approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

Exploring bulk volume, particle size and particle motion definitions to increase the predictive ability of in vitro dissolution simulations

The definition of the local dissolution environment is central to accurate particle dissolution simulation, and is determined by the apparatus and conditions used. In the flow-through apparatus dissolution occurs in the cell, often in a low velocity environment, with the reservoir considered the relevant volume for dissolution kinetics. Dissolution simulations were conducted using a reduced-order model based on the Ranz-Marshall correlation for mass transfer from spherical particles. Using ibuprofen as a model drug, the effect of defining a local volume to simulate dynamic bulk concentration conditions in the flow-through and paddle apparatus was assessed by comparing use of a near particle volume (NPV), extending a distance of one radius from the particle surface, with a flow-through apparatus cell volume or paddle apparatus vessel volume as the relevant instantaneous volume for dissolution. The instantaneous inlet concentration to NPV or cell volume is the reservoir/vessel concentration at that simulation time point, reflecting the continuous input to the cell of more dilute solution from the reservoir (closed system). Additionally, inputting particle size distribution (PSD) instead of a median particle size (MPS) and enabling or disabling particle motion were investigated, in two media (resulting in low and high solubility) and with two fluid velocity conditions in each apparatus. The NPV predicted effects of fluid velocity differences on dissolution in the high solubility medium in the flow-through apparatus, but had no effect on predictive ability in the paddle apparatus. In both apparatuses, simulations were reasonable for the high solubility environment but underpredicted dissolution in the low solubility environment. The PSD option and disabling particle motion increased the predictive ability of the simulations in low solubility media in the flow-through apparatus. The results highlight the necessity to incorporate the local dynamic dissolution conditions in the flow-through apparatus for accurate dissolution simulation, and the challenges of defining an effective particle size for dissolution simulation and of reflecting hydrodynamic complexity in simulating dissolution in the paddle apparatus.

Drug Solubility Correlation Using the Jouyban–Acree Model: Effects of Concentration Units and Error Criteria

An important factor affecting the model accuracy is the unit expression type for solute and solvent concentrations. One can report the solute and solvent concentration in various units and compare them with various error scales. In order to investigate the unit and error scale expression effects on the accuracy of the Jouyban–Acree model, in the current study, seventy-nine solubility data sets were collected randomly from the published articles and solute and solvent concentrations in the investigated systems were expressed in various units. Mass fraction, mole fraction, and volume fraction were the employed concentration units for the solvent compositions, and mole fraction, molar, and gram/liter were the investigated concentration units for the solutes. The solubility data, with various solute/solvent concentration units, were correlated using the Jouyban–Acree model, and the accuracy of each model for correlating the data was investigated by calculating different error scales and discussed.

Discovery solubility measurement and assessment of small molecules with drug development in mind

Solubility is a key physicochemical property for the success of any drug candidate. Although the methods used and their rationales for determining solubility are subject to project needs and stages along the drug discovery-drug development pipeline, an artificial boundary can exist at the discovery-development interface. This boundary results in less effective solubility knowledge sharing and data integration among scientists in both drug discovery and drug development. Herein, we present a refreshed perspective on solubility. Solubility experimentation is not a one-size-fits-all measurement; instead, we stress the importance of constructing a seamless solubility understanding of a molecule as it progresses from a new chemical entity into a drug product.

Determination of the aqueous pKa of very insoluble drugs by capillary electrophoresis: Internal standards for methanol-water extrapolation

A fast determination of acidity constants (pK_a) of very insoluble drugs has become a necessity in drug discovery process because it often produces molecules that are highly lipophilic and sparingly soluble in water. In this work the high throughput internal standard capillary electrophoresis (IS-CE) method has been adapted to the determination of pK_a of water insoluble compounds by measurement in methanol/aqueous buffer mixtures. For this purpose, the reference pK_a values for a set of 46 acid-base compounds of varied structure (internal standards) have been established in methanol-water mixtures at several solvent composition levels (with a maximum of 40% methanol). The IS-CE method has been successfully applied to seven test drugs of different chemical nature with intrinsic solubilities lower than 10^-6 M. pK_a values have been determined at different methanol/aqueous buffer compositions and afterwards Yasuda-Shedlovsky extrapolation method has been applied to obtain the aqueous pK_a. The obtained results have successfully been compared to literature ones obtained by other methods. It is concluded that the IS-CE method allows the determination of aqueous pK_a values using low proportions of methanol, becoming then more accurate in the extrapolation procedure than other reference methods.

Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge

The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pK_a and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.

UNGAP best practice for improving solubility data quality of orally administered drugs

An important goal of the European Cooperation in Science and Technology (COST) Action UNGAP (UNderstanding Gastrointestinal Absorption-related Processes, www.ungap.eu) is to improve standardization of methods relating to the study of oral drug absorption. Solubility is a general term that refers to the maximum achievable concentration of a compound dissolved in a liquid medium. For orally administered drugs, relevant information on drug properties is crucial during drug (product) development and at the regulatory level. Collection of reliable and reproducible solubility data requires careful application and understanding of the limitations of the selected experimental method. In addition, the purity of a compound and its solid state form, as well as experimental parameters such as temperature of experimentation, media related factors, and sample handling procedures can affect data quality. In this paper, an international consensus developed by the COST UNGAP network on recommendations for collecting high quality solubility data for the development of orally administered drugs is proposed.

The effect of buffer species on biorelevant dissolution and precipitation assays - Comparison of phosphate and bicarbonate buffer

Biorelevant solubility and dissolution testing is an important tool during pharmaceutical development, however, solubility experiments performed using biorelevant media often do not properly match the solubility data observed in human intestinal fluids. Even though the bicarbonate buffer is the predominant buffer system in the small intestine, in vitro assays are commonly performed using non-volatile buffer systems like phosphate and maleate. In the current study, bicarbonate- and phosphate-buffered biorelevant media were applied to solubility, dissolution, and precipitation testing for a broad range of model compounds. It was found that the medium affects primarily the dissolution kinetics. However, with the knowledge of the unique buffering properties of bicarbonate buffer in the diffusion layer, it was not always possible to predict the effect of buffer species on solubility and dissolution when changing from phosphate to bicarbonate buffer. This once again highlights the special role of bicarbonate buffer for simulating the conditions in the human intestinal fluids. Moreover, it is necessary to further investigate the factors which may cause the differences in solubility and dissolution behavior when using phosphate- vs. bicarbonate-buffered biorelevant media.

Hinge Binder Scaffold Hopping Identifies Potent Calcium/Calmodulin-Dependent Protein Kinase Kinase 2 (CAMKK2) Inhibitor Chemotypes

CAMKK2 is a serine/threonine kinase and an activator of AMPK whose dysregulation is linked with multiple diseases. Unfortunately, STO-609, the tool inhibitor commonly used to probe CAMKK2 signaling, has limitations. To identify promising scaffolds as starting points for the development of high-quality CAMKK2 chemical probes, we utilized a hinge-binding scaffold hopping strategy to design new CAMKK2 inhibitors. Starting from the potent but promiscuous disubstituted 7-azaindole GSK650934, a total of 32 compounds, composed of single-ring, 5,6-, and 6,6-fused heteroaromatic cores, were synthesized. The compound set was specifically designed to probe interactions with the kinase hinge-binding residues. Compared to GSK650394 and STO-609, 13 compounds displayed similar or better CAMKK2 inhibitory potency in vitro, while compounds 13g and 45 had improved selectivity for CAMKK2 across the kinome. Our systematic survey of hinge-binding chemotypes identified several potent and selective inhibitors of CAMKK2 to serve as starting points for medicinal chemistry programs.

Metabolic and process engineering for microbial production of protocatechuate with Corynebacterium glutamicum

3,4-Dihydroxybenzoate (protocatechuate, PCA) is a phenolic compound naturally found in edible vegetables and medicinal herbs. PCA is of high interest in the chemical industry and has wide potential for pharmaceutical applications. We designed and constructed a novel Corynebacterium glutamicum strain to enable the efficient utilization of d-xylose for microbial production of PCA. Shake flask cultivation of the engineered strain showed a maximum PCA titer of 62.1 ± 12.1 mM (9.6 ± 1.9 g L^-1 ) from d-xylose as the primary carbon and energy source. The corresponding yield was 0.33 C-mol PCA per C-mol d-xylose, which corresponds to 38% of the maximum theoretical yield. Under growth-decoupled bioreactor conditions, a comparable PCA titer and a total amount of 16.5 ± 1.1 g PCA could be achieved when d-glucose and d-xylose were combined as orthogonal carbon substrates for biocatalyst provision and product synthesis, respectively. Downstream processing of PCA was realized via electrochemically induced crystallization by taking advantage of the pH-dependent properties of PCA. This resulted in a maximum final purity of 95.4%. The established PCA production process represents a highly sustainable approach, which will serve as a blueprint for the bio-based production of other hydroxybenzoic acids from alternative sugar feedstocks.

Fast-Dissolving Solid Dispersions for the Controlled Release of Poorly Watersoluble Drugs

Solid dispersions offer many advantages for oral drug delivery of poorly water-soluble drugs over other systems, including an increase in drug solubility and drug dissolution. An improvement in drug absorption and the higher bioavailability of active pharmaceutical ingredients in the gastrointestinal tract have been reported in various studies. In certain circumstances, a rapid pharmacological effect is required for patients. Fastdissolving solid dispersions provide an ideal formulation in such cases. This report will provide an overview of current studies on fast-dissolving solid dispersions, including not only solid dispersion powders with fast dissolution rates but also specific dose form for the controlled release of poorly water-soluble drugs. Specifically, the applications of fast-dissolving solid dispersions will be described in every specific case. Moreover, pharmaceutical approaches and the utilization of polymers will be summarized. The classification and analysis of fastdissolving solid dispersions could provide insight into strategies and potential applications in future drug delivery developments.