An overview of drug discovery and development

An in-depth review of AI-powered advancements in cancer drug discovery

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies. Additionally, advancements like AlphaFold highlight AI's capacity to address intricate challenges in drug development. However, the field faces significant challenges, including the management of large-scale genomic datasets and ethical concerns surrounding AI deployment in healthcare. This review underscores the promise of data-centric AI approaches and emphasizes the necessity of continued innovation and interdisciplinary collaboration. Together, AI and genomics are charting a path toward more precise, efficient, and transformative cancer therapeutics.

New drug discovery and Hedonic Q: A new interpretation

Valuing intangible assets is crucial for both shareholders and stakeholders. This study revisits the Hedonic Q approach in the context of new drug discovery, employing a heterogeneous, time-varying difference-in-differences methodology to examine its effect on Hedonic Q. The findings suggest that new drug discovery has a positive and competitive impact on Hedonic Q, albeit with a lagged effect. Products in the early phases of drug discovery, including Phases I, II, and III, do not yield a positive impact on valuation in the short term. This study introduces an innovative framework to analyze the effect of new drug discovery on firm valuation in the pharmaceutical sector.

A multiplex method for rapidly identifying viral protease inhibitors

With current treatments addressing only a fraction of pathogens and new viral threats constantly evolving, there is a critical need to expand our existing therapeutic arsenal. To speed the rate of discovery and better prepare against future threats, we establish a high-throughput platform capable of screening compounds against 40 diverse viral proteases simultaneously. This multiplex approach is enabled by using cellular biosensors of viral protease activity combined with DNA-barcoding technology, as well as several design innovations that increase assay sensitivity and correct for plate-to-plate variation. Among >100,000 compound-target interactions explored within our initial screen, a series of broad-acting inhibitors against coronavirus proteases were uncovered and validated through orthogonal assays. A medicinal chemistry campaign was performed to improve one of the inhibitor's potency while maintaining its broad activity. This work highlights the power of multiplex screening to efficiently explore chemical space at a fraction of the time and costs of previous approaches.

Isolation of Biomolecules Using MXenes

Biomolecule isolation is a crucial process in diverse biomedical and biochemical applications, including diagnostics, therapeutics, research, and manufacturing. Recently, MXenes, a novel class of two-dimensional nanomaterials, have emerged as promising adsorbents for this purpose due to their unique physicochemical properties. These biocompatible and antibacterial nanomaterials feature a high aspect ratio, excellent conductivity, and versatile surface chemistry. This timely review explores the potential of MXenes for isolating a wide range of biomolecules, such as proteins, nucleic acids, and small molecules, while highlighting key future research trends and innovative applications poised to transform the field. This review provides an in-depth discussion of various synthesis methods and functionalization techniques that enhance the specificity and efficiency of MXenes in biomolecule isolation. In addition, the mechanisms by which MXenes interact with biomolecules are elucidated, offering insights into their selective adsorption and customized separation capabilities. This review also addresses recent advancements, identifies existing challenges, and examines emerging trends that may drive the next wave of innovation in this rapidly evolving area.

Discovery of a novel DYRK1A inhibitor with neuroprotective activity by virtual screening and in vitro biological evaluation

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is implicated in accumulation of amyloid β-protein (Aβ) and phosphorylation of Tau proteins, and thus represents an important therapeutic target for neurodegenerative diseases. Though many DYRK1A inhibitors have been discovered, there is still no marketed drug targeting DYRK1A. This is partly due to the lack of effective and safe chemotypes. Therefore, it is still necessary to identify new classes of DYRK1A inhibitors. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following DYRK1A inhibition assay, we identified compound L9, ((Z)-1-(((5-phenyl-1H-pyrazol-4-yl)methylene)-amino)-1H-tetrazol-5-amine), as a moderately active DYRK1A inhibitor (IC50: 1.67 μM). This compound was structurally different from the known DYRK1A inhibitors, showed a unique binding mode to DYRK1A. Furthermore, compound L9 showed neuroprotective activity against okadaic acid (OA)-induced injury in the human neuroblastoma cell line SH-SY5Y by regulating the expression of Aβ and phosphorylation of Tau protein. This compound was neither toxic to the SH-SY5Y cells nor to the human normal liver cell line HL-7702 (IC50: >100 μM). In conclusion, we have identified a novel DYRK1A inhibitor with neuroprotective activity through virtual screening and in vitro biological evaluation, which holds the promise for further study.

DynHeter-DTA: Dynamic Heterogeneous Graph Representation for Drug-Target Binding Affinity Prediction

In drug development, drug-target affinity (DTA) prediction is a key indicator for assessing the drug's efficacy and safety. Despite significant progress in deep learning-based affinity prediction approaches in recent years, there are still limitations in capturing the complex interactions between drugs and target receptors. To address this issue, a dynamic heterogeneous graph prediction model, DynHeter-DTA, is proposed in this paper, which fully leverages the complex relationships between drug-drug, protein-protein, and drug-protein interactions, allowing the model to adaptively learn the optimal graph structures. Specifically, (1) in the data processing layer, to better utilize the similarities and interactions between drugs and proteins, the model dynamically adjusts the connection strengths between drug-drug, protein-protein, and drug-protein pairs, constructing a variable heterogeneous graph structure, which significantly improves the model's expressive power and generalization performance; (2) in the model design layer, considering that the quantity of protein nodes significantly exceeds that of drug nodes, an approach leveraging Graph Isomorphism Networks (GIN) and Self-Attention Graph Pooling (SAGPooling) is proposed to enhance prediction efficiency and accuracy. Comprehensive experiments on the Davis, KIBA, and Human public datasets demonstrate that DynHeter-DTA exceeds the performance of previous models in drug-target interaction forecasting, providing an innovative solution for drug-target affinity prediction.

Special Issue "Advances in Drug Discovery and Synthesis"

In modern medicinal chemistry, drug discovery is a long, difficult, highly expensive and highly risky process for the identification of new drug compounds [...].

Researching public health datasets in the era of deep learning: a systematic literature review

Objective: Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current landscape. Materials and Methods: A systematic literature review was conducted in June 2023 to search articles on public health data in the context of deep learning, published from the inception of medical and computer science databases through June 2023. The review focused on diverse datasets, abstracting applications, challenges, and advancements in deep learning. Results: 2004 articles were reviewed, identifying 14 disease categories. Observed trends include explainable-AI, patient embedding learning, and integrating different data sources and employing deep learning models in health informatics. Noted challenges were technical reproducibility and handling sensitive data. Discussion: There has been a notable surge in deep learning applications on public health data publications since 2015. Consistent deep learning applications and models continue to be applied across public health data. Despite the wide applications, a standard approach still does not exist for addressing the outstanding challenges and issues in this field. Conclusion: Guidelines are needed for applying deep learning and models in public health data to improve FAIRness, efficiency, transparency, comparability, and interoperability of research. Interdisciplinary collaboration among data scientists, public health experts, and policymakers is needed to harness the full potential of deep learning.

DSE-HNGCN: Predicting the frequencies of drug-side effects based on heterogeneous networks with mining interactions between drugs and side effects

Evaluating the frequencies of drug-side effects is crucial in drug development and risk-benefit analysis. While existing deep learning methods show promise, they have yet to explore using heterogeneous networks to simultaneously model the various relationship between drugs and side effects, highlighting areas for potential enhancement. In this study, we propose DSE-HNGCN, a novel method that leverages heterogeneous networks to simultaneously model the various relationships between drugs and side effects. By employing multi-layer graph convolutional networks, we aim to mine the interactions between drugs and side effects to predict the frequencies of drug-side effects. To address the over-smoothing problem in graph convolutional networks and capture diverse semantic information from different layers, we introduce a layer importance combination strategy. Additionally, we have developed an integrated prediction module that effectively utilizes drug and side effect features from different networks. Our experimental results, using benchmark datasets in a range of scenarios, show that our model outperforms existing methods in predicting the frequencies of drug-side effects. Comparative experiments and visual analysis highlight the substantial benefits of incorporating heterogeneous networks and other pertinent modules, thus improving the accuracy of DSE-HNGCN predictions. We also provide interpretability for DSE-HNGCN, indicating that the extracted features are potentially biologically significant. Case studies validate our model's capability to identify potential side effects of drugs, offering valuable insights for subsequent biological validation experiments.

Structure-guided identification of potential inhibitors of MurB from S. typhimurium LT2 strain: towards therapeutic development against multidrug resistance

MurB or UDP-N-acetylenolpyruvoylglucosamine reductase (EC 1.3.1.98) is involved in the synthesis of bacterial cell walls of Salmonella typhimurium LT2 as it catalyzes one of the reactions in the formation of peptidoglycan. Since the enzyme is required for bacterial survival and is not present in humans, this makes it an ideal drug target, for multidrug resistance (MDR) strains. Thus, we proceeded with the identification of novel inhibitors of MurB that could overcome the existing resistance. The potential leads were identified from the PubChem library by selecting compounds with high structural similarity to the known inhibitors of MurB. These compounds were then taken through molecular docking studies and were further assessed based on physicochemical and ADMET characteristics. Regarding binding efficiency and drug-likeliness, two hit molecules with PubChem CID:10416900 and CID:14163894 were identified against MurB. Both compounds were closely bound to the MurB active site and did not induce any substantial structural changes in the MurB structure during all-atom molecular dynamics (MD) simulations and MM-PBSA studies. These compounds showed higher potential than the existing inhibitors and stood out as promising leads for the development of therapeutic inhibitors of MurB. The findings of the study, therefore, point to the viability of these compounds in the treatment of bacterial infections, thus enhancing the quality of patient care and disease management. More studies and experimental validation are required to explore their clinical use to the optimum.

Antagonising Yin Yang 1 ameliorates the symptoms of lupus nephritis via modulating T lymphocyte signaling

Lupus nephritis (LN) is a chronic complication of systemic lupus erythematosus (SLE). At present, no drugs are capable of delaying the progression of LN without a risk of serious side effects. There is thus a pressing need for further studies of LN pathogenesis to identify novel therapeutic targets and aid in the development of new approaches to treating this debilitating disease. In this study, a multi-omics approach was used to characterize the pathogenesis of LN and to identify disease-related targets, ultimately leading to the identification and validation of Yin Yang 1 (YY1) as a promising therapeutic target in LN. A rapid approach to efficiently screening for candidate YY1 ligands was implemented using drug databases that established rebamipide as a YY1 antagonist suitable for use in the management of LN. Specifically, the YY1 antagonist activity of rebamipide was found to regulate lymphocyte activity, reduce autoantibody production, limit immune complex deposition, and suppress macrophage activation while improving symptoms in a murine model of LN. Results supportive of a similar pathologic mechanism of action were also obtained when analyzing renal tissue sections from LN patients, underscoring the potential clinical significance of YY1 and its antagonist rebamipide, suggesting that rebamipide may have positive effects on lymphocytes and may improve symptoms in treated patients. This study provides a robust foundation for further research focused on the pathogenesis and treatment of LN.

Discovering novel Cathepsin L inhibitors from natural products using artificial intelligence

Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders. Current pharmacological interventions targeting CTSL have demonstrated potential in reducing body weight gain, serum insulin levels, and improving glucose tolerance. However, the clinical application of CTSL inhibitors remains limited. In this study, we used a combination of artificial intelligence and experimental methods to identify new CTSL inhibitors from natural products. Through a robust deep learning model and molecular docking, we screened 150 molecules from natural products for experimental validation. At a concentration of 100 µM, we found that 36 of them exhibited more than 50 % inhibition of CTSL. Notably, 13 molecules displayed over 90 % inhibition and exhibiting concentration-dependent effects. The molecular dynamics simulation on the two most potent inhibitors, Plumbagin and Beta-Lapachone, demonstrated stable interaction at the CTSL active site. Enzyme kinetics studies have shown that these inhibitors exert an uncompetitive inhibitory effect on CTSL. In conclusion, our research identifies Plumbagin and Beta-Lapachone as potential CTSL inhibitors, offering promising candidates for the treatment of metabolic disorders and illustrating the effectiveness of artificial intelligence in drug discovery.

Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy

In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach involved analyzing key interactions for FYN inhibition using established multi-kinase inhibitors such as Staurosporine, Dasatinib, and Saracatinib. We utilized ECFP4 circular fingerprints and the t-SNE machine learning algorithm to compare molecular similarities between FDA-approved drugs and known clinical trial inhibitors. This led to the identification of potential inhibitors, including Afatinib, Copanlisib, and Vandetanib. Using the DrugSpaceX platform, we generated a vast library of 72,196 analogues from these leads, which after careful refinement, resulted in 6008 promising candidates. Subsequent clustering identified 48 analogues with significant similarity to known inhibitors. Notably, two candidates derived from Vandetanib, DE27123047 and DE27123035, exhibited strong docking affinities and stable binding in molecular dynamics simulations. These candidates showed high potential as effective FYN kinase inhibitors, as evidenced by MMGBSA calculations and MCE-18 scores exceeding 50. Additionally, our exploration into their molecular architecture revealed potential modification sites on the quinazolin-4-amine scaffold, suggesting opportunities for strategic alterations to enhance activity and optimize ADME properties. Our research is a pioneering effort in drug discovery, unveiling novel candidates for FYN inhibition and demonstrating the efficacy of a multi-layered computational strategy. The molecular insights gained provide a pathway for strategic refinements and future experimental validations, setting a new direction in targeted drug development against diseases involving FYN kinase.

Design, Synthesis and Biological Exploration of Novel N-(9-Ethyl-9H-Carbazol-3-yl)Acetamide-Linked Benzofuran-1,2,4-Triazoles as Anti-SARS-CoV-2 Agents: Combined Wet/Dry Approach Targeting Main Protease (Mpro), Spike Glycoprotein and RdRp

A novel series of substituted benzofuran-tethered triazolylcarbazoles was synthesized in good to high yields (65-89%) via S-alkylation of benzofuran-based triazoles with 2-bromo-N-(9-ethyl-9H-carbazol-3-yl)acetamide. The inhibitory potency of the synthesized compounds against SARS-CoV-2 was evaluated by enacting molecular docking against its three pivotal proteins, namely, Mpro (main protease; PDB ID: 6LU7), the spike glycoprotein (PDB ID: 6WPT), and RdRp (RNA-dependent RNA polymerase; PDB ID: 6M71). The docking results indicated strong binding affinities between SARS-CoV-2 proteins and the synthesized compounds, which were thereby expected to obstruct the function of SARS proteins. Among the synthesized derivatives, the compounds 9e, 9h, 9i, and 9j exposited the best binding scores of -8.77, -8.76, -8.87, and -8.85 Kcal/mol against Mpro, respectively, -6.69, -6.54, -6.44, and -6.56 Kcal/mol against the spike glycoprotein, respectively, and -7.61, -8.10, -8.01, and -7.54 Kcal/mol against RdRp, respectively. Furthermore, the binding scores of 9b (-8.83 Kcal/mol) and 9c (-8.92 Kcal/mol) against 6LU7 are worth mentioning. Regarding the spike glycoprotein, 9b, 9d, and 9f expressed high binding energies of -6.43, -6.38, and -6.41 Kcal/mol, accordingly. Correspondingly, the binding affinity of 9g (-7.62 Kcal/mol) against RdRp is also noteworthy. Furthermore, the potent compounds were also subjected to ADMET analysis to evaluate their pharmacokinetic properties, suggesting that the compounds 9e, 9h, 9i, and 9j exhibited comparable values. These potent compounds may be selected as inhibitory agents and provide a pertinent context for further investigations.

TIWMFLP: Two-Tier Interactive Weighted Matrix Factorization and Label Propagation Based on Similarity Matrix Fusion for Drug-Disease Association Prediction

Accurately identifying new therapeutic uses for drugs is crucial for advancing pharmaceutical research and development. Matrix factorization is often used in association prediction due to its simplicity and high interpretability. However, existing matrix factorization models do not enable real-time interaction between molecular feature matrices and similarity matrices, nor do they consider the geometric structure of the matrices. Additionally, efficiently integrating multisource data remains a significant challenge. To address these issues, we propose a two-tier interactive weighted matrix factorization and label propagation model based on similarity matrix fusion (TIWMFLP) to assist in personalized treatment. First, we calculate the Gaussian and Laplace kernel similarities for drugs and diseases using known drug-disease associations. We then introduce a new multisource similarity fusion method, called similarity matrix fusion (SMF), to integrate these drug/disease similarities. SMF not only considers the different contributions represented by each neighbor but also incorporates drug-disease association information to enhance the contextual topological relationships and potential features of each drug/disease node in the network. Second, we innovatively developed a two-tier interactive weighted matrix factorization (TIWMF) method to process three biological networks. This method realizes for the first time the real-time interaction between the drug/disease feature matrix and its similarity matrix, allowing for a better capture of the complex relationships between drugs and diseases. Additionally, the weighted matrix of the drug/disease similarity matrix is introduced to preserve the underlying structure of the similarity matrix. Finally, the label propagation algorithm makes predictions based on the three updated biological networks. Experimental outcomes reveal that TIWMFLP consistently surpasses state-of-the-art models on four drug-disease data sets, two small molecule-miRNA data sets, and one miRNA-disease data set.

Repurposed pharmacotherapy: targeting cathepsin L with repurposed drugs in virtual screening

Proteolytic enzymes are closely associated with cancer and are important in different phases, including tumor growth, angiogenesis, and metastasis. Despite efforts to target matrix metalloproteases (MMPs), clinical trials have often resulted in various side effects such as musculoskeletal pain, joint stiffness, and tendinitis, making them less optimal for chronic cancer treatment. Thus, there is a need for the identification of other protease targets that would provide different approaches towards the management of cancer. Of these targets, Cathepsin L (CatL) is a lysosomal cysteine protease that has been identified as a therapeutic target that is implicated in cancer development and metastasis. In this study, we performed an integrated approach of virtual screening and molecular dynamics (MD) simulations to identify the potential inhibitors of CatL from a library of drugs that have been used for different treatments. Towards this goal, we performed virtual screening of the DrugBank database and found two repurposed drugs, Irinotecan and Nilotinib, against CatL based on their docking profiles, favorable docking scores, and specific interaction with the CatL binding pocket. MD simulations of the Irinotecan and Nilotinib bound structures with CatL were carried out, and the analysis showed that both these compounds could function as CatL inhibitors as the protein-ligand interactions were stable for 300 ns. This study highlights the robustness of these drugs bound to CatL and indicates that they could be repurposed for the treatment of cancer. These findings endorse the use of computer-based approaches for the identification of new inhibitors, and the present study will be a useful resource for future experimental research towards the targeting of CatL in cancer therapeutics.

Utilizing solid-state nanopore sensing for high-efficiency and precise targeted localization in antiviral drug development

The efficient identification and validation of drug targets are paramount in drug discovery and development. Excessive costs, intricate procedures, and laborious sample handling frequently encumber contemporary methodologies. In this study, we introduce an innovative approach for the expeditious screening of drug targets utilizing solid-state nanopores. These nanopores provide a label-free, ultra-sensitive, and high-resolution platform for the real-time detection of biomolecular interactions. By observing the changes in relative ion currents over time after mixing different peptides with small molecule drugs, and supplementing this with noise analysis, we can pinpoint specific regions of drug action, thereby enhancing both the speed and cost-efficiency of drug development. This research offers novel insights into drug discovery, expands current perspectives, and lays the groundwork for formulating effective therapeutic strategies across a spectrum of diseases.

Pharmaceutical Pollutants: Ecotoxicological Impacts and the Use of Agro-Industrial Waste for Their Removal from Aquatic Environments

Medicines are pharmaceutical substances used to treat, prevent, or relieve symptoms of different diseases in animals and humans. However, their large-scale production and use worldwide cause their release to the environment. Pharmaceutical molecules are currently considered emerging pollutants that enter water bodies due to inadequate management, affecting water quality and generating adverse effects on aquatic organisms. Hence, different alternatives for pharmaceuticals removal from water have been sought; among them, the use of agro-industrial wastes has been proposed, mainly because of its high availability and low cost. This review highlights the adverse ecotoxicological effects related to the presence of different pharmaceuticals on aquatic environments and analyzes 94 investigations, from 2012 to 2024, on the removal of 17 antibiotics, highlighting sulfamethoxazole as the most reported, as well as 6 non-steroidal anti-inflammatory drugs (NSAIDs) such as diclofenac and ibuprofen, and 27 pharmaceutical drugs with different pharmacological activities. The removal of these drugs was evaluated using agro-industrial wastes such as wheat straw, mung bean husk, bagasse, bamboo, olive stones, rice straw, pinewood, rice husk, among others. On average, 60% of the agro-industrial wastes were transformed into biochar to be used as a biosorbents for pharmaceuticals removal. The diversity in experimental conditions among the removal studies makes it difficult to stablish which agro-industrial waste has the greatest removal capacity; therefore, in this review, the drug mass removal rate (DMRR) was calculated, a parameter used with comparative purposes. Almond shell-activated biochar showed the highest removal rate for antibiotics (1940 mg/g·h), while cork powder (CP) (10,420 mg/g·h) showed the highest for NSAIDs. Therefore, scientific evidence demonstrates that agro-industrial waste is a promising alternative for the removal of emerging pollutants such as pharmaceuticals substances.

Perry Disease: Current Outlook and Advances in Drug Discovery Approach to Symptomatic Treatment

Perry disease (PeD) is a rare, neurodegenerative, genetic disorder inherited in an autosomal dominant manner. The disease manifests as parkinsonism, with psychiatric symptoms on top, such as depression or sleep disorders, accompanied by unexpected weight loss, central hypoventilation, and aggregation of DNA-binding protein (TDP-43) in the brain. Due to the genetic cause, no causal treatment for PeD is currently available. The only way to improve the quality of life of patients is through symptomatic therapy. This work aims to review the latest data on potential PeD treatment, specifically from the medicinal chemistry and computer-aided drug design (CADD) points of view. We select proteins that might represent therapeutic targets for symptomatic treatment of the disease: monoamine oxidase B (MAO-B), serotonin transporter (SERT), dopamine D2 (D2R), and serotonin 5-HT1A (5-HT1AR) receptors. We report on compounds that may be potential hits to develop symptomatic therapies for PeD and related neurodegenerative diseases and relieve its symptoms. We use Phase pharmacophore modeling software (version 2023.08) implemented in Schrödinger Maestro as a ligand selection tool. For each of the chosen targets, based on the resolved protein-ligand structures deposited in the Protein Data Bank (PDB) database, pharmacophore models are proposed. We review novel, active compounds that might serve as either hits for further optimization or candidates for further phases of studies, leading to potential use in the treatment of PeD.

Development of a carboxymethyl chitosan functionalized slide for small molecule detection using oblique-incidence reflectivity difference technology

Label-free optical biosensors have become powerful tools in the study of biomolecular interactions without the need for labels. High throughput and low detection limit are desirable for rapid and accurate biomolecule detection. The oblique-incidence reflectivity difference (OI-RD) technique is capable of detecting thousands of biomolecular interactions in a high-throughput mode, specifically for biomolecules larger than 1000 Da. In order to enhance the detection capability of OI-RD for small molecules (typically < 500 Da), we have developed a three-dimensional biochip that utilized carboxymethyl chitosan (CMCS) functionalized slides. By investigating various factors such as sonication time, protein immobilization time, CMCS molecular weight, and glutaraldehyde (GA) functionalization time, we have achieved a detection limit of 6.8 pM for avidin (68 kDa). Furthermore, accurate detection of D-biotin with a molecular weight of 244 Da has also been achieved. This paper presents an effective solution for achieving both high throughput and low detection limits using the OI-RD technique in the field of biomolecular interaction detection.

Revolutionizing Drug Discovery: The Impact of Distinct Designs and Biosensor Integration in Microfluidics-Based Organ-on-a-Chip Technology

Traditional drug development is a long and expensive process with high rates of failure. This has prompted the pharmaceutical industry to seek more efficient drug development frameworks, driving the emergence of organ-on-a-chip (OOC) based on microfluidic technologies. Unlike traditional animal experiments, OOC systems provide a more accurate simulation of human organ microenvironments and physiological responses, therefore offering a cost-effective and efficient platform for biomedical research, particularly in the development of new medicines. Additionally, OOC systems enable quick and real-time analysis, high-throughput experimentation, and automation. These advantages have shown significant promise in enhancing the drug development process. The success of an OOC system hinges on the integration of specific designs, manufacturing techniques, and biosensors to meet the need for integrated multiparameter datasets. This review focuses on the manufacturing, design, sensing systems, and applications of OOC systems, highlighting their design and sensing capabilities, as well as the technical challenges they currently face.

Repositioning of Small Molecules through the Inverse Virtual Screening in silico Tool: Case of Benzothiazole-Based Inhibitors of Soluble Epoxide Hydrolase (sEH)

Computational techniques accelerate drug discovery by identifying bioactive compounds for specific targets, optimizing molecules with moderate activity, or facilitating the repositioning of inactive items onto new targets. Among them, the Inverse Virtual Screening (IVS) approach is aimed at the evaluation of one or a small set of molecules against a panel of targets for addressing target identification. In this work, a focused library of benzothiazole-based compounds was re-investigated by IVS. Four items, originally synthesized and tested on bromodomain-containing protein 9 (BRD9) but yielding poor binding, were critically re-analyzed, disclosing only a partial fit with 3D structure-based pharmacophore models, which, in the meanwhile, were developed for this target. Afterwards, these compounds were re-evaluated through IVS on a panel of proteins involved in inflammation and cancer, identifying soluble epoxide hydrolase (sEH) as a putative interacting target. Three items were subsequently confirmed as able to interfere with sEH activity, leading to inhibition percentages spanning from 70 % up to 30 % when tested at 10 μM. Finally, one benzothiazole-based compound emerged as the most promising inhibitor featuring an IC50 in the low micromolar range (IC50=6.62±0.13 μM). Our data confirm IVS as a predictive tool for accelerating the target identification and repositioning processes.

Electrochemical Probing of Human Liver Subcellular S9 Fractions for Drug Metabolite Synthesis

Human liver subcellular fractions, including liver microsomes (HLM), liver cytosol fractions, and S9 fractions, are extensively utilized in in vitro assays to predict liver metabolism. The S9 fractions are supernatants of human liver homogenates that contain both microsomes and cytosol, which include most cytochrome P450 (CYP) enzymes and soluble phase II enzymes such as glucuronosyltransferases and sulfotransferases. This study reports on the direct electrochemistry and biocatalytic features of redox-active enzymes in S9 fractions for the first time. We investigated the electrochemical properties of S9 films by immobilizing them onto a high-purity graphite (HPG) electrode and performing cyclic voltammetry under anaerobic (Ar-saturated) and aerobic (O2-saturated) conditions. The heterogeneous electron transfer rate between the S9 film and the HPG electrode was found to be 14 ± 3 s-1, with a formal potential of -0.451 V vs. Ag/AgCl reference electrode, which confirmed the electrochemical activation of the FAD/FMN cofactor containing CYP450-reductase (CPR) as the electron receiver from the electrode. The S9 films have also demonstrated catalytic oxygen reduction under aerobic conditions, identical to HLM films attached to similar electrodes. Additionally, we investigated CYP activity in the S9 biofilm for phase I metabolism using diclofenac hydroxylation as a probe reaction and identified metabolic products using liquid chromatography-mass spectrometry (LC-MS). Investigating the feasibility of utilizing liver S9 fractions in such electrochemical assays offers significant advantages for pharmacological and toxicological evaluations of new drugs in development while providing valuable insights for the development of efficient biosensor and bioreactor platforms.

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs and diseases within a graph. However, labeled data for drug indication prediction is often limited and expensive to acquire. Contrastive learning addresses this challenge by aligning similar drug-disease pairs and separating dissimilar pairs in the embedding space. Thus, we developed a model called DrIGCL for drug indication prediction, which utilizes graph convolutional networks and contrastive learning. DrIGCL incorporates drug structure, disease comorbidities, and known drug indications to extract representations of drugs and diseases. By combining contrastive and classification losses, DrIGCL predicts drug indications effectively. In multiple runs of hold-out validation experiments, DrIGCL consistently outperformed existing computational methods for drug indication prediction, particularly in terms of top-k. Furthermore, our ablation study has demonstrated a significant improvement in the predictive capabilities of our model when utilizing contrastive learning. Finally, we validated the practical usefulness of DrIGCL by examining the predicted novel indications of Aspirin.

Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence

PURPOSE

Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes.

METHODS

A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes.

FINDINGS

The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs.

IMPLICATIONS

AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.

Adapting differential molecular representation with hierarchical prompts for multi-label property prediction

Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for Hierarchical Prompted Molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning.

Boosting the Accuracy and Chemical Space Coverage of the Detection of Small Colloidal Aggregating Molecules Using the BAD Molecule Filter

The ability to conduct effective high throughput screening (HTS) campaigns in drug discovery is often hampered by the detection of false positives in these assays due to small colloidally aggregating molecules (SCAMs). SCAMs can produce artifactual hits in HTS by nonspecific inhibition of the protein target. In this work, we present a new computational prediction tool for detecting SCAMs based on their 2D chemical structure. The tool, called the boosted aggregation detection (BAD) molecule filter, employs decision tree ensemble methods, namely, the CatBoost classifier and the light gradient-boosting machine, to significantly improve the detection of SCAMs. In developing the filter, we explore models trained on individual data sets, a consensus approach using these models, and, third, a merged data set approach, each tailored for specific drug discovery needs. The individual data set method emerged as most effective, achieving 93% sensitivity and 90% specificity, outperforming existing state-of-the-art models by 20 and 5%, respectively. The consensus models offer broader chemical space coverage, exceeding 90% for all testing sets. This feature is an important aspect particularly for early stage medicinal chemistry projects, and provides information on applicability domain. Meanwhile, the merged data set models demonstrated robust performance, with a notable sensitivity of 79% in the comprehensive 10-fold cross-validation test set. A SHAP analysis of model features indicates the importance of hydrophobicity and molecular complexity as primary factors influencing the aggregation propensity. The BAD molecule filter is readily accessible for the public usage on https://molmodlab-aau.com/Tools.html. This filter provides a new, more robust tool for aggregate prediction in the early stages of drug discovery to optimize hit rates and reduce associated testing and validation overheads.

Safety Implications of Modulating Nuclear Receptors: A Comprehensive Analysis from Non-Clinical and Clinical Perspectives

The unintended modulation of nuclear receptor (NR) activity by drugs can lead to toxicities amongst the endocrine, gastrointestinal, hepatic cardiovascular, and central nervous systems. While secondary pharmacology screening assays include NRs, safety risks due to unintended interactions of small molecule drugs with NRs remain poorly understood. To identify potential nonclinical and clinical safety effects resulting from functional interactions with 44 of the 48 human-expressed NRs, we conducted a systematic narrative review of the scientific literature, tissue expression data, and used curated databases (OFF-X™) (Off-X, Clarivate) to organize reported toxicities linked to the functional modulation of NRs in a tabular and machine-readable format. The top five NRs associated with the highest number of safety alerts from peer-reviewed journals, regulatory agency communications, congresses/conferences, clinical trial registries, and company communications were the Glucocorticoid Receptor (GR, 18,328), Androgen Receptor (AR, 18,219), Estrogen Receptor (ER, 12,028), Retinoic acid receptors (RAR, 10,450), and Pregnane X receptor (PXR, 8044). Toxicities associated with NR modulation include hepatotoxicity, cardiotoxicity, endocrine disruption, carcinogenicity, metabolic disorders, and neurotoxicity. These toxicities often arise from the dysregulation of receptors like Peroxisome proliferator-activated receptors (PPARα, PPARγ), the ER, PXR, AR, and GR. This dysregulation leads to various health issues, including liver enlargement, hepatocellular carcinoma, heart-related problems, hormonal imbalances, tumor growth, metabolic syndromes, and brain function impairment. Gene expression analysis using heatmaps for human and rat tissues complemented the functional modulation of NRs associated with the reported toxicities. Interestingly, certain NRs showed ubiquitous expression in tissues not previously linked to toxicities, suggesting the potential utilization of organ-specific NR interactions for therapeutic purposes.

A review of deep learning methods for ligand based drug virtual screening

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

Coupling of surface plasmon resonance and mass spectrometry for molecular interaction studies in drug discovery

Various analytical technologies have been developed for the study of target-ligand interactions. The combination of these technologies gives pivotal information on the binding mechanism, kinetics, affinity, residence time, and changes in molecular structures. Mass spectrometry (MS) offers structural information, enabling the identification and quantification of target-ligand interactions. Surface plasmon resonance (SPR) provides kinetic information on target-ligand interaction in real time. The coupling of MS and SPR complements each other in the studies of target-ligand interactions. Over the last two decades, the capabilities and added values of SPR-MS have been reported. This review summarizes and highlights the benefits, applications, and potential for further research of the SPR-MS approach.

Antifungal activity of propafenone on Candida spp. strains: interaction with antifungals and possible mechanism of action

Introduction. The development of new antifungal drugs has become a global priority, given the increasing cases of fungal diseases together with the rising resistance to available antifungal drugs. In this scenario, drug repositioning has emerged as an alternative for such development, with advantages such as reduced research time and costs.Gap statement. Propafenone is an antiarrhythmic drug whose antifungal activity is poorly described, being a good candidate for further study.Aim. This study aims to evaluate propafenone activity against different species of Candida spp. to evaluate its combination with standard antifungals, as well as its possible action mechanism.Methodology. To this end, we carried out tests against strains of Candida albicans, Candida auris, Candida parapsilosis, Candida tropicalis, Candida glabrata and Candida krusei based on the evaluation of the MIC, minimum fungicidal concentration and tolerance level, along with checkerboard and flow cytometry tests with clinical strains and cell structure analysis by scanning electron microscopy (SEM).Results. The results showed that propafenone has a 50% MIC ranging from 32 to 256 µg ml-1, with fungicidal activity and positive interactions with itraconazole in 83.3% of the strains evaluated. The effects of the treatments observed by SEM were extensive damage to the cell structure, while flow cytometry revealed the apoptotic potential of propafenone against Candida spp.Conclusion. Taken together, these results indicate that propafenone has the potential for repositioning as an antifungal drug.

Recent Advances in the Discovery of Novel Drugs on Natural Molecules

Natural products (NPs) are always a promising source of novel drugs for tackling unsolved diseases [...].

Exploring the mechanism of contact-dependent cell-cell communication on chemosensitivity based on single-cell high-throughput drug screening platform

High-throughput drug screening (HTDS) has significantly reduced the time and cost of new drug development. Nonetheless, contact-dependent cell-cell communication (CDCCC) may impact the chemosensitivity of tumour cells. There is a pressing need for low-cost single-cell HTDS platforms, alongside a deep comprehension of the mechanisms by which CDCCC affects drug efficacy, to fully unveil the efficacy of anticancer drugs. In this study, we develop a microfluidic chip for single-cell HTDS and evaluate the molecular mechanisms impacted by CDCCC using quantitative mass spectrometry-based proteomics. The chip achieves high-quality drug mixing and single-cell capture, with single-cell drug screening results on the chip showing consistency with those on the 96-well plates under varying concentration gradients. Through quantitative proteomic analysis, we deduce that the absence of CDCCC in single tumour cells can enhance their chemoresistance potential, but simultaneously subject them to stronger proliferation inhibition. Additionally, pathway enrichment analysis suggests that CDCCC could impact several signalling pathways in tumour single cells that regulate vital biological processes such as tumour proliferation, adhesion, and invasion. These results offer valuable insights into the potential connection between CDCCC and the chemosensitivity of tumour cells. This research paves the way for the development of single-cell HTDC platforms and holds the promise of advancing tumour personalized treatment strategies.

MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions

Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.

Phytotherapeutic options for the treatment of epilepsy: pharmacology, targets, and mechanism of action

Epilepsy is one of the most common, severe, chronic, potentially life-shortening neurological disorders, characterized by a persisting predisposition to generate seizures. It affects more than 60 million individuals globally, which is one of the major burdens in seizure-related mortality, comorbidities, disabilities, and cost. Different treatment options have been used for the management of epilepsy. More than 30 drugs have been approved by the US FDA against epilepsy. However, one-quarter of epileptic individuals still show resistance to the current medications. About 90% of individuals in low and middle-income countries do not have access to the current medication. In these countries, plant extracts have been used to treat various diseases, including epilepsy. These medicinal plants have high therapeutic value and contain valuable phytochemicals with diverse biomedical applications. Epilepsy is a multifactorial disease, and therefore, multitarget approaches such as plant extracts or extracted phytochemicals are needed, which can target multiple pathways. Numerous plant extracts and phytochemicals have been shown to treat epilepsy in various animal models by targeting various receptors, enzymes, and metabolic pathways. These extracts and phytochemicals could be used for the treatment of epilepsy in humans in the future; however, further research is needed to study the exact mechanism of action, toxicity, and dosage to reduce their side effects. In this narrative review, we comprehensively summarized the extracts of various plant species and purified phytochemicals isolated from plants, their targets and mechanism of action, and dosage used in various animal models against epilepsy.

Synthesis, characterization, molecular modeling, binding energies of β-cyclodextrin-inclusion complexes of quercetin: Modification of photo physical behavior upon β-CD complexation

We prepared a naturally occurring flavanoid namely quercetin from tea leaves and analyzed by Absorption, Emission, FT-IR, 1H, 13C nmr spectra and ESI-MS analysis. The inclusion behavior of quercetin in cyclodextrins like α-, β-, γ-, per-6-ABCD and mono-6-ABCD cavities were supported such as UV-vis., Emission, FT-IR and ICD spectra and energy minimization studies. From the absorption and emission results, the type of complexes formed were found to depend on stoichiometry of Host:Guest. FT-IR data of CD complexes of quercetin supported inclusion complex formation of the substrate with α-, β- and γ-CDs. The inclusion of host-guest complexation of quercetin with α-, β-, γ-CDs, per-6-ABCD and mono-6-ABCDs provides very valuable information about the CD:quercetin complexes, the study also shows that β-CD complexation improves water solubility, chemical stability and bioavailability of quercetin. Besides, phase solubility studies also supported the formation of 1:1 drug-CD soluble complexes. All these spectral results provide insight into the binding behavior of substrate into CD cavity in the order per-6-ABCD > Mono-6-ABCD > γ-CD > β-CD > α-CD. The proposed model also finds strong support from the fact with excess CD this exciton coupling disappears indicates the formation of only 1:1 complex.

A Comprehensive Study to Determine the Residual Elimination Pattern of Major Metabolites of Amoxicillin-Sulbactam Hybrid Molecules in Rats by UPLC-MS/MS

Amoxicillin and sulbactam are widely used in animal food compounding. Amoxicillin-sulbactam hybrid molecules are bicester compounds made by linking amoxicillin and sulbactam with methylene groups and have good application prospects. However, the residual elimination pattern of these hybrid molecules in animals needs to be explored. In the present study, the amoxicillin-sulbactam hybrid molecule (AS group) and a mixture of amoxicillin and sulbactam (mixture group) were administered to rats by gavage, and the levels of the major metabolites of amoxicillin, amoxicilloic acid, amoxicillin diketopiperazine, and sulbactam were determined by UPLC-MS/MS. The residue elimination patterns of the major metabolites in the liver, kidney, urine, and feces of rats in the AS group and the mixture group were compared. The results showed that the total amount of amoxicillin, amoxicilloic acid, amoxicillin diketopiperazine, and the highest concentration of sulbactam in the liver and kidney samples of the AS group and the mixture group appeared at 1 h after drug withdrawal. Between 1 h and 12 h post discontinuation, the total amount of amoxicillin, amoxicilloic acid, and amoxicillin diketopiperazine in the two tissues decreased rapidly, and the elimination half-life of the AS group was significantly higher than that in the mixture group (p < 0.05); the residual amount of sulbactam also decreased rapidly, and the elimination half-life was not significantly different (p > 0.05). In 72 h urine samples, the total excretion rates were 60.61 ± 2.13% and 62.62 ± 1.73% in the AS group and mixture group, respectively. The total excretion rates of fecal samples (at 72 h) for the AS group and mixture group were 9.54 ± 0.26% and 10.60 ± 0.24%, respectively. These results showed that the total quantity of amoxicillin, amoxicilloic acid, and amoxicillin diketopiperazine was eliminated more slowly in the liver and kidney of the AS group than those of the mixture group and that the excretion rate through urine and feces was essentially the same for both groups. The residual elimination pattern of the hybrid molecule in rats determined in this study provides a theoretical basis for the in-depth development and application of hybrid molecules, as well as guidelines for the development of similar drugs.

MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. The source code and experimental data for MIFAM-DTI are available at https://github.com/Search-AB/MIFAM-DTI.

Predictive insights into plant-based compounds as fibroblast growth factor receptor 1 inhibitors: a combined molecular docking and dynamics simulation study

The discovery of novel therapeutic agents with potent anticancer activity remains a critical challenge in drug development. Natural products, particularly bioactive phytoconstituents derived from plants, have emerged as promising sources for anticancer drug discovery. In this study, we used virtual screening techniques to explore the potential of bioactive phytoconstituents as inhibitors of fibroblast growth factor receptor 1 (FGFR1), a key signaling protein implicated in cancer progression. We used virtual screening techniques to analyze phytoconstituents extracted from the IMPPAT 2.0 database. Our primary objective was to discover promising inhibitors of FGFR1. To ensure the selection of promising candidates, we initially filtered the molecules based on their physicochemical properties. Subsequently, we performed binding affinity calculations, PAINS, ADMET, and PASS filters to identify nontoxic and highly effective hits. Through this screening process, one phytocompound, namely Mundulone, emerged as a potential lead. This compound demonstrated an appreciable affinity for FGFR1 and exhibited specific interactions with the ATP-binding site residues. To gain further insights into the conformational dynamics of Mundulone and the reference FGFR1 inhibitor, Lenvatinib, we conducted time-evolution analyses employing 200 ns molecular dynamics simulations (MDS) and essential dynamics. These analyses provided valuable information regarding the dynamic behavior and stability of the compounds in complexes with FGFR1. Overall, the findings indicate that Mundulone exhibits promising binding affinity, specific interactions, and favorable drug profiles, making it a promising lead candidate. Further experimental analysis will be necessary to confirm its effectiveness and safety profiles for therapeutic advancement in the cancer field.Communicated by Ramaswamy H. Sarma.

Design and Evaluation of NSAID Derivatives as AKR1C3 Inhibitors for Breast Cancer Treatment through Computer-Aided Drug Design and In Vitro Analysis

Breast cancer is a major global health issue, causing high incidence and mortality rates as well as psychological stress for patients. Chemotherapy resistance is a common challenge, and the Aldo-keto reductase family one-member C3 enzyme is associated with resistance to anthracyclines like doxorubicin. Recent studies have identified celecoxib as a potential treatment for breast cancer. Virtual screening was conducted using a quantitative structure-activity relationship model to develop similar drugs; this involved backpropagation of artificial neural networks and structure-based virtual screening. The screening revealed that the C-6 molecule had a higher affinity for the enzyme (-11.4 kcal/mol), a lower half-maximal inhibitory concentration value (1.7 µM), and a safer toxicological profile than celecoxib. The compound C-6 was synthesized with an 82% yield, and its biological activity was evaluated. The results showed that C-6 had a more substantial cytotoxic effect on MCF-7 cells (62%) compared to DOX (63%) and celecoxib (79.5%). Additionally, C-6 had a less harmful impact on healthy L929 cells than DOX and celecoxib. These findings suggest that C-6 has promising potential as a breast cancer treatment.

Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

Computational biology approaches for drug repurposing

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro

Here, we introduce the use of ANI-ML potentials as a rescoring function in the host-guest interaction in molecular docking. Our results show that the "docking power" of ANI potentials can compete with the current scoring functions at the same level of computational cost. Benchmarking studies on CASF-2016 dataset showed that ANI is ranked in the top 5 scoring functions among the other 34 tested. In particular, the ANI predicted interaction energies when used in conjunction with GOLD-PLP scoring function can boost the top ranked solution to be the closest to the x-ray structure. Rapid and accurate calculation of interaction energies between ligand and protein also enables screening of millions of drug candidates/docking poses. Using a unique protocol in which docking by GOLD-PLP, rescoring by ANI-ML potentials and extensive MD simulations along with end state free energy methods are combined, we have screened FDA approved drugs against the SARS-CoV-2 main protease (Mpro). The top six drug molecules suggested by the consensus of these free energy methods have already been in clinical trials or proposed as potential drug molecules in previous theoretical and experimental studies, approving the validity and the power of accuracy in our screening method.

The therapeutic potential of natural metabolites in targeting endocrine-independent HER-2-negative breast cancer

Breast cancer (BC) is a heterogenous disease, with prognosis and treatment options depending on Estrogen, Progesterone receptor, and Human Epidermal Growth Factor Receptor-2 (HER-2) status. HER-2 negative, endocrine-independent BC presents a significant clinical challenge with limited treatment options. To date, promising strategies like immune checkpoint inhibitors have not yielded breakthroughs in patient prognosis. Despite being considered archaic, agents derived from natural sources, mainly plants, remain backbone of current treatment. In this context, we critically analyze novel naturally-derived drug candidates, elucidate their intricate mechanisms of action, and evaluate their pre-clinical in vitro and in vivo activity in endocrine-independent HER-2 negative BC. Since pre-clinical research success often does not directly correlate with drug approval, we focus on ongoing clinical trials to uncover current trends. Finally, we demonstrate the potential of combining cutting-edge technologies, such as antibody-drug conjugates or nanomedicine, with naturally-derived agents, offering new opportunities that utilize both traditional cytotoxic agents and new metabolites.

Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction

BACKGROUND

Identification of potential drug-disease associations is important for both the discovery of new indications for drugs and for the reduction of unknown adverse drug reactions. Exploring the potential links between drugs and diseases is crucial for advancing biomedical research and improving healthcare. While advanced computational techniques play a vital role in revealing the connections between drugs and diseases, current research still faces challenges in the process of mining potential relationships between drugs and diseases using heterogeneous network data.

RESULTS

In this study, we propose a learning framework for fusing Graph Transformer Networks and multi-aggregate graph convolutional network to learn efficient heterogenous information graph representations for drug-disease association prediction, termed WMAGT. This method extensively harnesses the capabilities of a robust graph transformer, effectively modeling the local and global interactions of nodes by integrating a graph convolutional network and a graph transformer with self-attention mechanisms in its encoder. We first integrate drug-drug, drug-disease, and disease-disease networks to construct heterogeneous information graph. Multi-aggregate graph convolutional network and graph transformer are then used in conjunction with neural collaborative filtering module to integrate information from different domains into highly effective feature representation.

CONCLUSIONS

Rigorous cross-validation, ablation studies examined the robustness and effectiveness of the proposed method. Experimental results demonstrate that WMAGT outperforms other state-of-the-art methods in accurate drug-disease association prediction, which is beneficial for drug repositioning and drug safety research.

Navigating the Frontiers of Machine Learning in Neurodegenerative Disease Therapeutics

Recent advances in machine learning hold tremendous potential for enhancing the way we develop new medicines. Over the years, machine learning has been adopted in nearly all facets of drug discovery, including patient stratification, lead discovery, biomarker development, and clinical trial design. In this review, we will discuss the latest developments linking machine learning and CNS drug discovery. While machine learning has aided our understanding of chronic diseases like Alzheimer's disease and Parkinson's disease, only modest effective therapies currently exist. We highlight promising new efforts led by academia and emerging biotech companies to leverage machine learning for exploring new therapies. These approaches aim to not only accelerate drug development but to improve the detection and treatment of neurodegenerative diseases.

The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.