CMMS-GCL: Cross-Modality Metabolic Stability Prediction with Graph Contrastive Learning.

Poor metabolic stability leads to drug development failure. Therefore, it is essential to evaluate the metabolic stability of small compounds for successful drug discovery and development. However, evaluating metabolic stability in vitro and in vivo is expensive, time-consuming and laborious. In addition, only a few free software programs are available for metabolic stability data and prediction. Therefore, in this study, we aimed to develop a prediction model that predicts the metabolic stability of small compounds. We developed a computational model, PredMS, which predicts the metabolic stability of small compounds as stable or unstable in human liver microsomes. PredMS is based on a random forest model using an in-house database of metabolic stability data of 1917 compounds. To validate the prediction performance of PredMS, we generated external test data of 61 compounds. PredMS achieved an accuracy of 0.74, Matthew's correlation coefficient of 0.48, sensitivity of 0.70, specificity of 0.86, positive predictive value of 0.94 and negative predictive value of 0.46 on the external test dataset. PredMS will be a useful tool to predict the metabolic stability of small compounds in the early stages of drug discovery and development. The source code for PredMS is available at https://bitbucket.org/krictai/predms, and the PredMS web server is available at https://predms.netlify.app. Supplementary data are available at Bioinformatics online.

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software.

An alternative biomimetic tool – Dual hydrophilic/reversed-phase interaction mode

Overview of Drug Polypharmacology and Multitargeted Molecular Design

Author index

ABSTRACTPharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies.

A goal of preclinical discovery is the identification of drug candidates suitable for clinical testing. Successful integration of in vitro and in vivo experimental data sets can afford projections of human dose regimens anticipated to be safe and therapeutically beneficial. While in vitro experiments guide new chemical syntheses and are essential to understanding drug action and disposition, in vivo characterizations provide unique insight into complex biological systems that control concentrations at the site of action and pharmacologic response. Pharmacokinetic and pharmacodynamic (PK/PD) concepts underlying drug disposition and response provide a quantitative framework with which to identify potential clinical candidates. To improve throughput in earlier stages of drug discovery, in vivo pharmacokinetic study designs such as cassette dosing and sparse sampling schemes have been utilized. In later stages of discovery, pharmacokinetic studies using chemical inhibitors or surgical and genetic animal models are used to characterize the underlying determinants of drug disposition. In a complimentary fashion, modeling of in vivo pharmacodynamic effects may quantitatively link biomarkers to pharmacological response, validate in vitro to in vivo correlations and underwrite predictions of efficacious exposure targets. When applied to in vivo discovery data, PK/PD models have aided in understanding mechanisms of pharmacological response such as receptor theory in the central nervous system and cell turnover concepts in infectious disease and oncology. This review considers the role of in vivo testing toward understanding the pharmacokinetic and pharmacodynamic attributes of lead candidates in drug discovery.

Structure-based virtual screening is highly used in the early stages of drug discovery to identify new putative lead compounds for a given target. However, when a small molecule elicits a biological effect, but its target is unknown, or the side effects it causes arise from its undesired interaction with unknown counterparts, the identification of its interacting targets represents an indispensable task. The computational procedure named inverse virtual screening, which relies on docking a molecule (or a small set of compounds) against panels of target proteins to select the most promising complexes, could be useful to overcome these issues. Panels can contain thousands of proteins, and they must be correctly prepared to assure the best docking performance. Therefore, the preparation of panels of proteins collected in the Protein Data Bank ( www.rcsb.org ), if manually performed, may be costly in terms of time and efforts, and this can limit the applicability of this approach in high-throughput virtual screening workflows. We here show an automated workflow to speed up panel preparation and development, and to test its performance, this protocol was initially applied to a panel of 628 viral proteins and, afterward, to a panel of transferase proteins (2789 entries) to perform a large inverse virtual screening study, testing a small set of compounds synthesized in our laboratory. Tankyrase 2 (PARP 5b) was selected as their preferred target of interaction, and the predicted binding was validated by means of surface plasmon resonance experiments. This protocol is useful for the rapid identification of the interacting target for a bioactive compound; accordingly, it facilitates the re-evaluation of the pharmacological activity of known active compounds, addressing the repurposing and the polypharmacology concepts.

Electrochemistry in combination with mass spectrometry is emerging as a versatile analytical technique in the imitation of oxidative drug metabolism during the early stages of drug discovery and development. Here, we present electrochemical O-dealkylation of phenacetin to acetaminophen by square-wave potential pulses consisting of consecutive sub-second oxidation and reduction steps. This O-dealkylation could not be achieved by oxidation at constant potential or longer potential pulses because of the fast hydrolysis of the reactive intermediates. Electrochemical conversion by square-wave potential pulses can thus widen the scope of electrochemical synthesis of metabolites and imitation of in vivo drug metabolism.

<b>Abstract ID 25135</b> <b>Poster Board 182</b> Metabolite identification (MetID) studies provide critical information to elucidate the biotransformation pathways of a new chemical entity during the early stages of drug discovery and development. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the gold-standard technique for prediction of metabolite structures, however, the current MetID studies rely on manual data processing. The goal of our study was to analyze LC-HRMS data using a high-throughput metabolomics-based MetID workflow by utilizing an in-house theoretical metabolite predictor and open-access XCMS Online (xcmsonline.scripps.edu). In particular, we applied this approach to detect and identify biotransformation products of cimetidine, probenecid, and imatinib in blood and urine samples collected from three rat pharmacokinetic studies. Briefly, rats (n= 4 to 8) were dosed with placebo (control) or drugs [cimetidine (100 mg/kg, IP), probenecid (1000 mg/kg, IV), and imatinib (30 mg/kg, PO)], and the blood and urine samples were collected at various time points/intervals. The control and drug-treated samples were individually processed by protein precipitation followed by drying and reconstitution in LC-MS compatible solvents. The pooled blood (0.5, 1, and 2 h) and urine (0-4 h) samples of cimetidine and probenecid, and pooled blood (1, 2, and 4 h) samples of imatinib were individually analyzed using nanoLC coupled Thermo Q-Exactive HF mass spectrometer. Data-independent acquisition method was applied to allow detection of the low level metabolites. Cimetidine, probenecid, and imatinib treated samples and the corresponding controls were analyzed by XCMS Online software. Lists of all potential theoretical metabolites and their high-resolution masses were created for these three drugs using a novel in-house theoretical metabolite predictor. The output excel file was first processed to shortlist the potential metabolites using the following optimized criteria: a) metabolite elevated in the treatment group by fold difference of at least 2.5, b) accurate mass within ± 5 ppm of predicted theoretical mass, c) no detectable peak in any control samples and distinguishable peak in all treatment samples, and d) mass-defect shift and the relative retention time within a reasonable range of theoretically possible value. Then, the shortlisted data were searched against the theoretical metabolite list in a semi-automated fashion to identify drug metabolites (Figure 1). Cimetidine-treated rats showed 5 and 6 metabolite hits (3 common) in blood and urine, respectively, which included novel dihydroxyl, carboxyl, and N-acetylcysteine derivatives of cimetidine. Similarly, 6 and 14 potential metabolites (4 common) were detected in the probenecid-treated rat blood and urine, respectively. Of these, dihydroxyl, glycine and glucosylated derivatives of probenecid were detected for the first time. Five reported metabolites were detected and identified in imatinib-treated rat blood samples. Thus, a sensitive and semi-automated workflow was developed for high-throughput identification of putative biotransformation products of drugs in biological samples, which is not only applicable in drug MetID studies, but it is potentially useful in generating xenobiotic metabolite libraries to facilitate exposomics research.

Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism

Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions. We propose a computational framework, DeepHIT, which predicts hERG blockers and non-blockers for input compounds. For the development of DeepHIT, we generated a large-scale gold-standard dataset, which includes 6632 hERG blockers and 7808 hERG non-blockers. DeepHIT is designed to contain three deep learning models to improve sensitivity and NPV, which, in turn, produce fewer false negative predictions. DeepHIT outperforms currently available tools in terms of accuracy (0.773), MCC (0.476), sensitivity (0.833) and NPV (0.643) on an external test dataset. We also developed an in silico chemical transformation module that generates virtual compounds from a seed compound, based on the known chemical transformation patterns. As a proof-of-concept study, we identified novel urotensin II receptor (UT) antagonists without hERG-blocking activity derived from a seed compound of a previously reported UT antagonist (KR-36676) with a strong hERG-blocking activity. In summary, DeepHIT will serve as a useful tool to predict hERG-induced cardiotoxicity of small compounds in the early stages of drug discovery and development. https://bitbucket.org/krictai/deephit and https://bitbucket.org/krictai/chemtrans. Supplementary data are available at Bioinformatics online.

Kinetic solubility and lipophilicity evaluation connecting formulation technology strategy perspective

Blockade of human ether-à-go-go related gene (hERG) channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining quantitative structure-activity relationship (QSAR) modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high-predictive capacity and improved enrichment and could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed, and molecular docking studies were conducted, resulting in high correlations (R² = 0.81) between predicted and experimental pIC₅₀s. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities.

Integrating multiple omics to unravel mechanisms of Cyclosporin A induced hepatotoxicity in vitro