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Antibody-oligonucleotide conjugates (AOCs) are promising treatments for Duchenne muscular dystrophy (DMD). They work via induction of exon skipping and restoration of dystrophin protein in skeletal and heart muscles. The structure-activity relationships (SARs) of AOCs comprising antibody-phosphorodiamidate morpholino oligomers (PMOs) depend on several aspects of their component parts. We evaluate the SAR of antimouse transferrin receptor 1 antibody (αmTfR1)-PMO conjugates: cleavable and noncleavable linkers, linker location on the PMO, and the impact of drug-to-antibody ratios (DARs) on plasma pharmacokinetics (PK), oligonucleotide delivery to tissues, and exon skipping. AOCs containing a stable linker with a DAR9.7 were the most effective PMO delivery vehicles in preclinical studies. We demonstrate that αmTfR1-PMO conjugates induce dystrophin protein restoration in the skeletal and heart muscles of mdx mice. Our results show that αmTfR1-PMO conjugates are a potentially effective approach for the treatment of DMD.

Antibody-oligonucleotide conjugates are a promising class of therapeutics for extrahepatic delivery of small interfering ribonucleic acids (siRNAs). These conjugates can be optimized for improved delivery and mRNA knockdown (KD) through understanding of structure-activity relationships. In this study, we systematically examined factors including antibody isotype, siRNA chemistry, linkers, conjugation chemistry, PEGylation, and drug-to-antibody ratios (DARs) for their impact on bioconjugation, pharmacokinetics (PK), siRNA delivery, and bioactivity. Conjugation site (cysteine, lysine, and Asn297 glycan) and DAR proved critical for optimal conjugate PK and siRNA delivery. SiRNA chemistry including 2' sugar modifications and positioning of phosphorothioates were found to be critical for delivery and duration of action. By utilizing cleavable and noncleavable linkers, we demonstrated the impact of linkers on PK and mRNA KD. To achieve optimal properties of antibody-siRNA conjugates, a careful selection of siRNA chemistry, DAR, conjugation sites, linkers, and antibody isotype is necessary.

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.

Human African trypanosomiasis (HAT) still faces few therapeutic options and emerging drug resistance, stressing an urgency for novel antitrypanosomal drug discovery. Here, we describe lead optimization efforts aiming at improving antitrypanosomal efficacy and better physicochemical properties based on our previously reported optimized hit NPD-2975 (pIC 50 7.2). Systematic modification of the 5-phenylpyrazolopyrimidinone NPD-2975 led to the discovery of a R 4 -substituted analogue 31c (NPD-3519), showing higher in vitro potency (pIC 50 7.8) against Trypanosoma brucei and significantly better metabolic stability. Further, in vivo pharmacokinetic evaluation of 31c and experiments in an acute T. brucei mouse model confirmed improved oral bioavailability and antitrypanosomal efficacy at 50 mg/kg with no apparent toxicity. With good physicochemical properties, low toxicity, improved pharmacokinetic features, and in vivo efficacy, 31c may serve as a promising candidate for future drug development for HAT.

Several lines of evidence have indicated that nicotinic acetylcholine receptors (nAChR) that contain α9 subunits, probably in combination with α10 subunits, may be valuable targets for the management of pain associated with inflammatory diseases through a cholinergic anti-inflammatory system (CAS), which has also been associated with α7 nAChR. Both α7- and α9-containing neuronal nAChR can be pharmacologically distinguished from the high-affinity nicotinic receptors of the brain by their sensitivity to α-bungarotoxin, but in other ways, they have quite distinct pharmacological profiles. The early association of α7 with CAS led to the development of numerous new ligands, variously characterized as α7 agonists, partial agonists, or silent agonists that desensitized α7 receptors without activation. Subsequent reinvestigation of one such family of α7 ligands based on an N , N -diethyl- N '-phenylpiperazine scaffold led to the identification of potent agonists and antagonists for α9. In this paper, we characterize the α9/α10 activity of a series of compounds based on a 5-(quinuclidin-3-ylmethyl)-1,2,4-oxadiazole (QMO) scaffold and identify two new potent ligands of α9, QMO-28, an agonist, and QMO-17, an antagonist. We separated the stereoisomers of these compounds to identify the most potent agonist and discovered that only the 3 R isomer of QMO-17 was an α9 antagonist, permitting an in silico model of α9 antagonism to be developed. The α9 activity of these compounds was confirmed to be potentially useful for CAS management of inflammatory pain in cell-based assays of cytokine release.

Sirtuins catalyze deacetylation of lysine residues with a NAD + -dependent mechanism. In mammals, the sirtuin family is composed of seven members, divided into four subclasses that differ in substrate specificity, subcellular localization, regulation, as well as interactions with other proteins, both within and outside the epigenetic field. Recently, much interest has been growing in SIRT3, which is mainly involved in regulating mitochondrial metabolism. Moreover, SIRT3 seems to be protective in diseases such as age-related, neurodegenerative, liver, kidney, heart, and metabolic ones, as well as in cancer. In most cases, activating SIRT3 could be a promising strategy to tackle these health problems. Here, we summarize the main biological functions, substrates, and interactors of SIRT3, as well as several molecules reported in the literature that are able to modulate SIRT3 activity. Among the activators, some derive from natural products, others from library screening, and others from the classical medicinal chemistry approach.

Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement.

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. State-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

Technologies currently employed to find and identify drug metabolites in complex biological matrices generally yield results that offer a comprehensive picture of the drug metabolite profile. However, drug metabolites can be missed or are captured only late in the drug development process. This could be due to a variety of factors, such as metabolism that results in partial loss of the molecule, covalent bonding to macromolecules, the drug being metabolized in specific human tissues, or poor ionization in a mass spectrometer. These scenarios often draw a great deal of attention from chemistry, safety assessment, and pharmacology. This review will summarize scenarios of missing metabolites, why they are missing, and associated uncovering strategies from deeper investigations. Uncovering previously missed metabolites can have ramifications in drug development with toxicological and pharmacological consequences, and knowledge of these can help in the design of new drugs.

The birth of ChatGPT, a cutting-edge language model-based chatbot developed by OpenAI, ushered in a new era in AI. However, due to potential pitfalls, its role in rigorous scientific research is not clear yet. This paper vividly showcases its innovative application within the field of drug discovery. Focused specifically on developing anticocaine addiction drugs, the study employs GPT-4 as a virtual guide, offering strategic and methodological insights to researchers working on generative models for drug candidates. The primary objective is to generate optimal drug-like molecules with desired properties. By leveraging the capabilities of ChatGPT, the study introduces a novel approach to the drug discovery process. This symbiotic partnership between AI and researchers transforms how drug development is approached. Chatbots become facilitators, steering researchers toward innovative methodologies and productive paths for creating effective drug candidates. This research sheds light on the collaborative synergy between human expertise and AI assistance, wherein ChatGPT's cognitive abilities enhance the design and development of pharmaceutical solutions. This paper not only explores the integration of advanced AI in drug discovery but also reimagines the landscape by advocating for AI-powered chatbots as trailblazers in revolutionizing therapeutic innovation.

Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).

ABBV-167, a phosphate prodrug of BCL-2 inhibitor venetoclax, was recently progressed into the clinic as an alternative means of reducing pill burden for patients in high-dose indications. The dramatically enhanced aqueous solubility of ABBV-167 allowed for high drug loading within a crystalline tablet and, when administered in phase I clinical study, conferred venetoclax exposure commensurate with the equivalent dose administered as an amorphous solid dispersion. In enabling the progression into the clinic, we performed a comprehensive evaluation of the CMC development aspects of this beyond the rule of five (bRo5) prodrug. Adding a phosphate moiety resulted in excessively complex chemical speciation and solid form landscapes with significant physical-chemical stability liabilities. A combination of experimental and computational methods including microelectron diffraction (MicroED), total scattering, tablet colorimetry, finite element, and molecular dynamics modeling were used to understand CMC developability across drug substance and product manufacture and storage. The prodrug's chemical structural characteristics and loose crystal packing were found to be responsible for the loss of crystallinity during its manufacturing, which in turn led to high solid-state chemical reactivity and poor shelf life stability. The ABBV-167 case exemplifies key CMC development challenges for complex chemical matter such as bRo5 phosphate prodrugs with significant ramifications during drug substance and drug product manufacturing and storage.

Various approaches have been developed to enhance the solubility or dissolution rate for the delivery of poorly water-soluble molecules. In this work, guided by an in silico solubility sensitivity analysis for oral absorption, a comparative assessment of the biopharmaceutical performance of a jet-milled free base, a tosylate salt, and a 50:50 (w/w) amorphous solid dispersion (ASD) with hydroxypropyl methylcellulose acetate succinate (HPMCAS) of a weak base drug candidate, GDC-3280, was conducted. Successful particle size reduction without amorphization or form change was confirmed for the jet-milled free base. The potential of solubility enhancement and desupersaturation risk were identified for tosylate salt and ASD formulation by measurements of tosylate salt solubility product constant ( K sp ) and amorphous solubility of GDC-3280. In vitro dissolution testing demonstrated dissolution rate improvement for the jet-milled free base when compared with the unmilled free base and confirmed solubility enhancement followed by desupersaturation for GDC-3280 tosylate salt and ASD formulation. A crystallization inhibitor, hydroxypropyl methylcellulose (HPMC), was found to slow down the desupersaturation of tosylate salt solution, providing general insights for the development of pharmaceutical salts with disproportionation risks. Finally, a pharmacokinetic study in dogs showed that the in vivo exposure increased by 1.7- to 2-fold for the tosylate salt and ASD formulation compared with the jet-milled free base, consistent with the in silico solubility sensitivity analysis for the fraction of drug absorbed. Overall, this work provides insights into the evaluation of multiple formulation approaches for enhancing the biopharmaceutical performance of poorly water-soluble drugs.

Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profile of drugs can be directly and considerably altered by modulating factors affecting absorption. Many drugs in development fail because of poor absorption. The research and continuous efforts of researchers in recent years have brought many successes and promises in drug absorption property prediction, especially in silico , which helps to reduce the time and cost significantly for screening undesirable drug candidates. In this report, we explicitly provide an overview of recent in silico studies on predicting absorption properties, especially from 2019 to the present, using artificial intelligence. Additionally, we have collected and investigated public databases that support absorption prediction research. On those grounds, we also proposed the challenges and development directions of absorption prediction in the future. We hope this review can provide researchers with valuable guidelines on absorption prediction to facilitate the development of newer approaches in drug discovery.

Mechanical behavior of pharmaceutical crystals directly impacts the formulation development and manufacturing of drug products. The understanding of crystal structure-mechanical behavior of pharmaceutical and molecular crystals has recently gained substantial attention among pharmaceutical and materials scientists with the advent of advanced nanomechanical testing instruments like nanoindentation. For the past few decades, instrumented nanoindentation was a popular technique for measuring the mechanical properties of thin films and small-length scale materials. More recently it is being implemented to investigate the mechanical properties of pharmaceutical crystals. Integration of correlative microscopy techniques and environmental control opened the door for advanced structure-property correlation under processing conditions. Preventing the degradation of active pharmaceutical ingredients from external factors such as humidity, temperature, or pressure is important during processing. This review deals with the recent developments in the synchronized nanomechanical measurements of pharmaceutical crystals toward the fast and effective development of high-quality pharmaceutical drug products. This review also summarizes some recent reports to intensify how one can design and control the nanomechanical properties of pharmaceutical solids. Measurement challenges and the scope for studying nanomechanical properties of pharmaceutical crystals using nanoindentation as a function of crystal structure and in turn to develop fundamental knowledge in the structure-property relationship with the implications for drug manufacturing and development are discussed in this review. This review further highlights recently developed capabilities in nanoindentation, for example, variable temperature nanoindentation testing, in situ imaging of the indented volume, and nanoindentation coupled Raman spectroscopy that can offer new quantitative details on nanomechanical behavior of crystals and will play a decisive role in the development of coherent theories for nanomechanical study of pharmaceutical crystal.

Membrane ion channels are the fundamental electrical components in the nervous system. Recent developments in X-ray crystallography and cryo-EM microscopy have revealed what these proteins look like in atomic detail but do not tell us how they function. Molecular dynamics simulations have progressed to the point that we can now simulate realistic molecular assemblies to produce quantitative calculations of the thermodynamic and kinetic quantities that control function. In this review, we summarize the state of atomistic simulation methods for ion channels to understand their conduction, activation, and drug modulation mechanisms. We are at a crossroads in atomistic simulation, where long time scale observation can provide unbiased exploration of mechanisms, supplemented by biased free energy methodologies. We illustrate the use of these approaches to describe ion conduction and selectivity in voltage-gated sodium and acid-sensing ion channels. Studies of channel gating present a significant challenge, as activation occurs on longer time scales. Enhanced sampling approaches can ensure convergence on minimum free energy pathways for activation, as illustrated here for pentameric ligand-gated ion channels that are principal to nervous system function and the actions of general anesthetics. We also examine recent studies of local anesthetic and antiepileptic drug binding to a sodium channel, revealing sites and pathways that may offer new targets for drug development. Modern simulations thus offer a range of molecular-level insights into ion channel function and modulation as a learning platform for mechanistic discovery and drug development. [ABSTRACT FROM AUTHOR]

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Human African Trypanosomiasis (HAT), caused by Trypanosoma brucei , is one of the neglected tropical diseases with a continuing need for new medication. We here describe the discovery of 5-phenylpyrazolopyrimidinone analogs as a novel series of phenotypic antitrypanosomal agents. The most potent compound, 30 (NPD-2975), has an in vitro IC 50 of 70 nM against T. b. brucei with no apparent toxicity against human MRC-5 lung fibroblasts. Showing good physicochemical properties, low toxicity potential, acceptable metabolic stability, and other pharmacokinetic features, 30 was further evaluated in an acute mouse model of T. b. brucei infection. After oral dosing at 50 mg/kg twice per day for five consecutive days, all infected mice were cured. Given its good drug-like properties and high in vivo antitrypanosomal potential, the 5-phenylpyrazolopyrimidinone analog 30 represents a promising lead for future drug development to treat HAT.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Therapeutically relevant proteins naturally adsorb to interfaces, causing aggregation which in turn potentially leads to numerous adverse consequences such as loss of activity or unwanted immunogenic reactions. Surfactants are ubiquitously used in biotherapeutics drug development to oppose interfacial stress, yet, the choice of the surfactant is extremely limited: to date, only polysorbates (PS20/80) and poloxamer 188 are used in commercial products. However, both surfactant families suffer from severe degradation and impurities of the raw material, which frequently increases the risk of particle generation, chemical protein degradation, and potential adverse immune reactions. Herein, we assessed a total of 40 suitable alternative surfactant candidates and subsequently performed a selection through a three-gate screening process employing four protein modalities encompassing six different formulations. The screening is based on short-term agitation-induced aggregation studies coupled to particle analysis and surface tension characterization, followed by long-term quiescence stability studies connected to protein purity measurements and particle analysis. The study concludes by assessing the surfactant's chemical and enzymatic degradation propensity. The candidates emerging from the screening are de novo α-tocopherol-derivatives named VEDG-2.2 and VEDS, produced ad hoc for this study. They display protein stabilization potential comparable or better than polysorbates together with an increased resistance to chemical and enzymatic degradation, thus representing valuable alternative surfactants for biotherapeutics.

Proteolysis-targeting chimera (PROTAC) technology represents a novel and promising modality for targeted protein degradation with transformative implications for the clinical management of various diseases. Despite notable advantages, the possibility of on-target off-tumor toxicity in healthy cells represents a critical challenge to clinical applications in cancer treatment. Researchers are currently exploring strategies to enhance targeted degradation activity in a cell-selective manner to minimize undesirable side effects. In this Perspective, we highlight innovative approaches for prodrug-based PROTACs (pro-PROTACs) that facilitate tumor-targeted release. The development of such approaches may further expand the range of potential applications of PROTAC technology within drug development.

Autophagy is a lysosome-dependent mechanism of intracellular degradation for maintaining cellular homeostasis. Dysregulation of autophagy has been verified to be closely linked to a number of human diseases. Consequently, targeting autophagy has been highlighted as a novel therapeutic strategy for clinical utility. Mounting efforts have been done in recent years to elucidate the mechanisms of autophagy regulation and to identify potential modulators of autophagy. However, most of the compounds target complex and multifaceted pathway and proteins, which may limit the evaluation of therapeutic value and in depth studies as chemical tools. Therefore, the development of specific and active autophagy modulators becomes most desirable. Here, we briefly review the regulation of autophagy and then summarize the recent development of small molecules targeting the core autophagic machinery. Finally, we put forward our viewpoints on the current problems, with the aim to provide reference for future drug discovery and potential therapeutic perspectives on novel, potent, selective autophagy modulators. [ABSTRACT FROM AUTHOR]

Residence time and more recently the association rate constant kon are increasingly acknowledged as important parameters for in vivo efficacy and safety of drugs. However, their broader consideration in drug development is limited by a lack of knowledge of how to optimize these parameters. In this study on a set of 176 heat shock protein 90 inhibitors, structure-kinetic relationships, X-ray crystallography, and molecular dynamics simulations were combined to retrieve a concrete scheme of how to rationally slow down on-rates. We discovered that an increased ligand desolvation barrier by introducing polar substituents resulted in a significant kon decrease. The slowdown was accomplished by introducing polar moieties to those parts of the ligand that point toward a hydrophobic cavity. We validated this scheme by increasing polarity of three Hsp90 inhibitors and observed a 9-, 13-, and 45-fold slowdown of on-rates and a 9-fold prolongation in residence time. This prolongation was driven by transition state destabilization rather than ground state stabilization. [ABSTRACT FROM AUTHOR]

The epidermal growth factor receptor (EGFR) has been a particular interest for drug development for treatment of non-small-cell lung cancer (NSCLC). The current third-generation EGFR small-molecule inhibitors, especially osimertinib, are at the forefront clinically for treatment of patients with NSCLC. However, a high percentage of these treated patients developed a tertiary cystein-797 to serine-790 (C797S) mutation in the EGFR kinase domain. This C797S mutation is thought to induce resistance to all current irreversible EGFR TKIs. In this Miniperspective, we present key mechanisms of resistance in response to third-generation EGFR TKIs, and emerging reports on novel EGFR TKIs to combat the resistance. Specifically, we analyze the allosteric and ATP-competitive inhibitors in terms of drug discovery, binding mechanism, and their potency and selectivity against EGFR harboring C797S mutations. Lastly, we provide some perspectives on new challenges and future directions in this field. [ABSTRACT FROM AUTHOR]

Antibiotic resistance of bacterial pathogens poses an increasing threat to the wellbeing of our society and urgently calls for new strategies for infection diagnosis and antibiotic discovery. The antibiotic resistance problem at least partially arises from extensive use of broad-spectrum antibiotics. Ideally, for the treatment of infection, one would like to use a narrow-spectrum antibiotic that specifically targets and kills the disease-causing strain. This is particularly important considering the commensal bacterial species that are beneficial and sometimes even critical to the health of a human being. In this contribution, we describe a phage display platform that enables rapid identification of peptide probes for specific bacterial strains. The phage library described herein incorporates 2-acetylphenyl-boronic acid moieties to elicit dynamic covalent binding to the bacterial cell surface. Screening of the library against live bacterial cells yields submicromolar and highly specific binders for clinical strains of Staphylococcus aureus and Acinetobacter baumannii that display antibiotic resistance. We further show that the identified peptide probes can be readily converted to bactericidal agents that deliver generic toxins to kill the targeted bacterial strain with high specificity. The phage display platform described here is applicable to a wide array of bacterial strains, paving the way to facile diagnosis and development of strain-specific antibiotics. [ABSTRACT FROM AUTHOR]

Compounds containing a difluoromethylene unit have gained increasing attention due to their utility in drug design. Classic methods for the synthesis of these compounds rely on either harsh deoxofluorination reactions or laborious functional group manipulation sequences. In 2013, we proposed a method for assembling gem-difluorinated molecules from a difluorocarbene, a nucleophile, and an electrophile. In this process, a difluorocarbene can be considered an equivalent of a bipolar CF2 unit. Performing consecutive bond-forming reactions by sequential attachment of a nucleophile and an electrophile to a difluorocarbene provides the opportunity for the synthesis of a wide variety of organofluorine compounds. Silicon reagents were the most effective sources of the difluoromethylene fragment, and among them (bromodifluoromethyl)trimethylsilane (Me3SiCF2Br) is the reagent of choice. Mildly basic activators such HMPA, DMPU, bromide and acetate ions can initiate the decomposition of the silane with concomitant generation of a difluorocarbene. Organozinc reagents can be employed as nucleophiles, and the CF2 fragment can insert into the carbon-zinc bond. Primary and secondary benzyl and alkyl organozinc compounds work well. Generally, organozinc reagents tolerate a variety of functional groups. The resulting fluorinated organozinc species can be coupled with heteroatom- or carbon-centered electrophiles. Halogenation of the carbon-zinc bond leads to compounds with bromo- or iododifluoromethyl fragments, which are difficult to access by other means, whereas protonation of that bond generates a valuable difluoromethyl group. Despite the decrease in the reactivity of the carbon-zinc bond caused by the adjacent fluorines, organozinc compounds can effectively participate in copper-catalyzed cross-couplings with allylic and propargyl halides, 1-bromoalkynes, and S-acyl dithiocarbamates. Difluorocarbene can be inserted into the carbon-silicon bond of trimethylsilyl cyanide, and the resulting silane can react with aldehydes and imines to furnish difluorinated nitriles. Interactions of difluorocarbene with heteroatom nucleophiles, such as phosphines or halide ions, are reversible, but the adduct can be trapped by an electrophile. The use of halide ions allows the direct nucleophilic bromo- and iododifluoromethylation of aldehydes and iminium ions. The combination of triphenylphosphine with difluorocarbene generates a difluorinated phosphorus ylide, which can interact with a wide range of π-electrophiles (aldehydes, ketones, acyl chlorides, azomethines, and Michael acceptors) to provide gem-difluorinated phosphonium salts. In the latter species, the carbon-phosphorus bond can be readily cleaved under basic conditions, affording the difluoromethylation products. Primary products resulting from three-component couplings can subsequently be used for further transformations. Single-electron reduction of carbon-phosphorus or carbon-iodine bonds can be conducted under photocatalytic conditions to generate gem-difluorinated radicals. These radicals can be trapped by silyl enol ethers leading to ß,ß-difluorinated ketones as the primary products. Fluorinated radicals can also undergo intramolecular attacks adjacent to an aromatic ring or a double bond. [ABSTRACT FROM AUTHOR]

We describe the discovery of MK-6169, a potent and pan-genotype hepatitis C virus NS5A inhibitor with optimized activity against common resistance-associated substitutions. SAR studies around the combination of changes to both the valine and aminal carbon region of elbasvir led to the discovery of a series of compounds with substantially improved potency against common resistance-associated substitutions in the major genotypes, as well as good pharmacokinetics in both rat and dog. Through further optimization of key leads from this effort, MK-6169 (21) was discovered as a preclinical candidate for further development. [ABSTRACT FROM AUTHOR]

We hypothesized that if drug localization can be restricted to a particular subcellular domain where their target proteins reside, the drugs could bind to their target proteins without being metabolized and/or excreted, which would significantly extend the half-life of the corresponding drug-target complex. Thus, we designed ligand-phospholipid conjugates in which the ligand is conjugated with a phospholipid through a polyethylene glycol linker to restrict the subcellular localization of the ligand in the vicinity of the lipid bilayer. Here, we present the design, synthesis, pharmacological activity, and binding mode analysis of ligand-phospholipid conjugates with muscarinic acetylcholine receptors as the target proteins. These results demonstrate that ligand-phospholipid conjugation can be a versatile strategy for developing long-acting ligands that bind to membrane proteins in drug discovery. [ABSTRACT FROM AUTHOR]

In order to optimize a lead molecule for further development, bioisosteric replacements are generally adopted as one of the strategies. Silicon appears to be the right choice as a carbon isostere because of the similarity in chemical properties. Silicon can be strategically introduced in a molecule to modulate its druglike properties, providing medicinal chemists with an unconventional strategy for replacing a carbon atom. Silicon can also be introduced to replace other heteroatoms and can act as a surrogate of functional groups such as olefin and amide as well. The present Perspective focuses on the opportunities that silicon incorporation offers in drug discovery, with an emphasis on case studies where introduction of silicon has created a benefit over its analog. We have tried to highlight all the recent developments in the field and briefly discuss the challenges associated with them. [ABSTRACT FROM AUTHOR]

Discovery of novel insulin analogs as therapeutics has remained an active area of research. Compared with native human insulin, insulin analog molecules normally incorporate either covalent modifications or amino acid sequence variations. From the drug discovery and development perspective, methods for efficient and detailed characterization of these primary structural changes are very important. In this report, we demonstrate that proteinase K digestion coupled with UPLC-ESI-MS analysis provides a simple and rapid approach to characterize the modifications and sequence variations of insulin molecules. A commercially available proteinase K digestion kit was used to process recombinant human insulin (RHI), insulin glargine, and fluorescein isothiocynate-labeled recombinant human insulin (FITC-RHI) samples. The LC-MS data clearly showed that RHI and insulin glargine samples can be differentiated, and the FITC modifications in all three amine sites of the RHI molecule are well characterized. The end-to-end experiment and data interpretation was achieved within 60 min. This approach is fast and simple, and can be easily implemented in early drug discovery laboratories to facilitate research on more advanced insulin therapeutics.ᅟ [ABSTRACT FROM AUTHOR]

The vitamin D3 receptor (VDR), which belongs to the nuclear-receptor superfamily, is a potential molecular target for anticancer-drug discovery. In this study, a series of nonsteroidal vitamin D mimics with phenyl-pyrrolyl pentane skeletons with therapeutic potentials in cancer treatment were synthesized. Among them, 11b and 11g were identified as the most effective agents in reducing the viability of four cancer-cell lines, particularly those of breast-cancer cells, with IC50 values in the submicromolar-concentration range. In addition, 11b and 11g possessed VDR-binding affinities and displayed significant partial VDR-agonistic activities determined by dual-luciferase-reporter assays and human-leukemia-cell-line (HL-60)-differentiation assays. Furthermore, 11b and 11g inhibited tumor growth in an orthotopic breast-tumor model via inhibition of cell proliferation and induction of cell apoptosis. More importantly, 11b and 11g exhibited favorable pharmacokinetic behavior in vivo and did not increase serum calcium levels or cause any other apparent side effects. In summary, 11b and 11g act as novel VDR modulators and may be promising candidates for cancer chemotherapy. [ABSTRACT FROM AUTHOR]

Recently, there has been an increasing focus on the pursuit of targets considered to be less druggable that offer potential for development of promising new therapeutic agents for the treatment of diseases with large unmet medical need, particularly in the areas of oncology and virology. However, conducting drug discovery campaigns in "beyond rule of 5" (bRo5) chemical space presents a significant drug design and development challenge to medicinal chemists to achieve acceptable oral pharmacokinetics. Retrospective analysis of past successes and failures in drug discovery bRo5 may shed light on the key principles that contribute to the oral bioavailability of successful bRo5 compounds and improve the efficiency of drug design for future projects. We present here highlights and case studies of lessons learned from discovery of bRo5 compounds. A simple multiparametric scoring function (AB-MPS) was devised that correlated preclinical PK results with cLogD, number of rotatable bonds, and number of aromatic rings. [ABSTRACT FROM AUTHOR]

In recent years, thymidylate kinase (TMPK), an enzyme indispensable for bacterial DNA biosynthesis, has been pursued for the development of new antibacterial agents including against Mycobacterium tuberculosis, the causative agent for the widespread infectious disease tuberculosis (TB). In response to a growing need for more effective anti-TB drugs, we have built upon our previous efforts toward the exploration of novel and potent Mycobacterium tuberculosis TMPK (MtTMPK) inhibitors, and reported here the design of a novel series of non-nucleoside inhibitors of MtTMPK. The inhibitors display hitherto unexplored interactions in the active site of MtTMPK, offering new insights into structure-activity relationships. To investigate the discrepancy between enzyme inhibitory activity and the whole-cell activity, experiments with efflux pump inhibitors and efflux pump knockout mutants were performed. The minimum inhibitory concentrations of particular inhibitors increased significantly when determined for the efflux pump mmr knockout mutant, which partly explains the observed dissonance. [ABSTRACT FROM AUTHOR]

Receptor interacting protein kinase 1 (RIPK1) plays a crucial role in tumor necrosis factor (TNF)-induced necroptosis, suggesting that this pathway might be druggable. Most inhibitors of RIPK1 are classified as either type II or type III kinase inhibitors. This opened up some interesting perspectives for the discovery of novel inhibitors that target the active site of RIPK1. Tozasertib, a type I pan-aurora kinase (AurK) inhibitor, was found to show a very high affinity for RIPK1. Because tozasertib presents the typical structural elements of a type I kinase inhibitor, the development of structural analogues of tozasertib is a good starting point for identifying novel type I RIPK1 inhibitors. In this paper, we identified interesting inhibitors of mTNF-induced necroptosis with no significant effect on AurK A and B, resulting in no nuclear abnormalities as is the case for tozasertib. Compounds 71 and 72 outperformed tozasertib in an in vivo TNF-induced systemic inflammatory response syndrome (SIRS) mouse model. [ABSTRACT FROM AUTHOR]

The popularity of fragment-based drug discovery (FBDD) is demonstrated by the number of recent successful fragment-to-lead (F2L) publications. This Miniperspective provides a tabulated summary of the F2L literature published in the year 2016, along with discussion of general trends. It uses the same format as our summary of the 2015 literature and is intended to be a resource for both FBDD practitioners and medicinal chemists in general. [ABSTRACT FROM AUTHOR]

Methods to incorporate deuterium and tritium atoms into organic molecules are valuable for medicinal chemistry. The prevalence of pyridines and diazines in pharmaceuticals means that new ways to label these heterocycles will present opportunities in drug design and facilitate absorption, distribution, metabolism, and excretion (ADME) studies. A broadly applicable protocol is presented wherein pyridines, diazines, and pharmaceuticals are converted into heterocyclic phosphonium salts and then isotopically labeled. The isotopes are incorporated in high yields and, in general, with exclusive regioselectivity. [ABSTRACT FROM AUTHOR]