Drug discovery and development: Role of basic biological research

Current developments and prospects of the antibiotic delivery systems

Antibiotics have remained the cornerstone for the treatment of bacterial infections ever since their discovery in the twentieth century. The uproar over antibiotic resistance among bacteria arising from genome plasticity and biofilm development has rendered current antibiotic therapies ineffective, urging the development of innovative therapeutic approaches. The development of antibiotic resistance among bacteria has further heightened the clinical failure of antibiotic therapy, which is often linked to its low bioavailability, side effects, and poor penetration and accumulation at the site of infection. In this review, we highlight the potential use of siderophores, antibodies, cell-penetrating peptides, antimicrobial peptides, bacteriophages, and nanoparticles to smuggle antibiotics across impermeable biological membranes to achieve therapeutically relevant concentrations of antibiotics and combat antimicrobial resistance (AMR). We will discuss the general mechanisms via which each delivery system functions and how it can be tailored to deliver antibiotics against the paradigm of mechanisms underlying antibiotic resistance.

Current status and perceived challenges of collaborative research in a leading pharmacy college in Iraq: a qualitative study

BACKGROUND

Interdisciplinary collaboration among academic pharmacists is crucial for enhancing scientific research, discovering new drugs and modifying existing ones, besides solving pharmaceutical problems. This study aimed to explore the perception and experience of academic pharmacists regarding research collaboration.

METHODS

A qualitative study through one-to-one face-to-face interviews with faculty members at the University of Baghdad/College of Pharmacy was conducted from May to July/2023. Purposive and convenience strategies were used to enroll study participants. Thematic-analysis approach was used to analyze the data.

RESULTS

Twenty-four faculty members were interviewed. Most participants were female with ≥ 10 years of academic experience. Five themes emerged from the obtained data. The first theme, entitled the collaborative research was conducted at three different levels: college, national, and to a lesser extent, international. The second theme was facilitators of collaborative research. This theme includes two subthemes the reasons behind research collaboration and the encouraging characteristics of the researcher. Seeking scientific and technical support were the main reported reasons behind domestic collaborations, while supervising postgraduate students was the main reason for international collaborations. The third theme was the barriers to collaborative research. The complicated-university laws, besides limited-resources & funds, were barriers to collaborative research. Academic workload was the main challenge for domestic collaborations, whereas poor professional-networking was the main challenge for international collaborations. Ethical challenge in collaborative research was the fourth study theme. The last theme was the recommendations to improve future research collaboration. In this regard, most participants recommended enhancing academics' research-skills, increasing research funding, and simplifying regulatory issues to improve collaborative research.

CONCLUSION

Research collaborations occur at the domestic and, to a limited extent, at the international level. Academic workload, shortage of resources, and complicated university regulations are the main challenges for research collaborations. Enhancing research-skills, increasing research funding, and simplifying university regulations can improve research collaborations.

Drug repositioning for Parkinson's disease: An emphasis on artificial intelligence approaches

Parkinson's disease (PD) is one of the most incapacitating neurodegenerative diseases (NDDs). PD is the second most common NDD worldwide which affects approximately 1-2 percent of people over 65 years. It is an attractive pursuit for artificial intelligence (AI) to contribute to and evolve PD treatments through drug repositioning by repurposing existing drugs, shelved drugs, or even candidates that do not meet the criteria for clinical trials. A search was conducted in three databases Web of Science, Scopus, and PubMed. We reviewed the data related to the last years (1975-present) to identify those drugs currently being proposed for repositioning in PD. Moreover, we reviewed the present status of the computational approach, including AI/Machine Learning (AI/ML)-powered pharmaceutical discovery efforts and their implementation in PD treatment. It was found that the number of drug repositioning studies for PD has increased recently. Repositioning of drugs in PD is taking off, and scientific communities are increasingly interested in communicating its results and finding effective treatment alternatives for PD. A better chance of success in PD drug discovery has been made possible due to AI/ML algorithm advancements. In addition to the experimentation stage of drug discovery, it is also important to leverage AI in the planning stage of clinical trials to make them more effective. New AI-based models or solutions that increase the success rate of drug development are greatly needed.

The cell replacement therapeutic potential of human pluripotent stem cells

INTRODUCTION

The remarkable ability of human pluripotent stem cells (hPSCs) to differentiate into specialized cells of the human body emphasizes their immense potential in treating various diseases. Advances in hPSC technology are paving the way for personalized and allogeneic cell-based therapies. The first-in-human studies showed improved treatment of diseases with no adverse effects, which encouraged the industrial production of this type of medicine. To ensure the quality, safety and efficacy of hPSC-based products throughout their life cycle, it is important to monitor and control their clinical translation through good practices (GxP) regulations. Understanding these rules in advance will help ensure that the industrial development of hPSC-derived products for widespread clinical implementation is feasible and progresses rapidly.

AREAS COVERED

In this review, we discuss the key translational obstacles of hPSCs, outline the current hPSC-based clinical trials, and present a workflow for putative clinical hPSC-based products. Finally, we highlight some future therapeutic opportunities for hPSC-derivatives.

EXPERT OPINION

hPSC-based products continue to show promise for the treatment of a variety of diseases. While clinical trials support the relative safety and efficacy of hPSC-based products, further investigation is required to explore the clinical challenges and achieve exclusive regulations for hPSC-based cell therapies.

Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications

Traditional drug discovery methods such as wet-lab testing, validations, and synthetic techniques are time-consuming and expensive. Artificial Intelligence (AI) approaches have progressed to the point where they can have a significant impact on the drug discovery process. Using massive volumes of open data, artificial intelligence methods are revolutionizing the pharmaceutical industry. In the last few decades, many AI-based models have been developed and implemented in many areas of the drug development process. These models have been used as a supplement to conventional research to uncover superior pharmaceuticals expeditiously. AI's involvement in the pharmaceutical industry was used mostly for reverse engineering of existing patents and the invention of new synthesis pathways. Drug research and development to repurposing and productivity benefits in the pharmaceutical business through clinical trials. AI is studied in this article for its numerous potential uses. We have discussed how AI can be put to use in the pharmaceutical sector, specifically for predicting a drug's toxicity, bioactivity, and physicochemical characteristics, among other things. In this review article, we have discussed its application to a variety of problems, including de novo drug discovery, target structure prediction, interaction prediction, and binding affinity prediction. AI for predicting drug interactions and nanomedicines were also considered.

Leaflet: Operative Steps for Interventional Studies in Neuroscience

BACKGROUND/OBJECTIVES

Drug development involves multiple stages, spanning from initial discovery to clinical trials. This intricate process entails understanding disease mechanisms, identifying potential drug targets, and evaluating the efficacy and safety of candidate drugs. Clinical trials are designed to assess the effects of drugs on humans, focusing on determining safety profiles, appropriate modes of administration, and comparative efficacy against placebos. Notably, neuroscience drug development encounters distinct challenges, including the complex nature of diseases, limitations imposed by the blood-brain barrier, the absence of reliable predictive preclinical models, and regulatory hurdles. Ethical and safety considerations are pivotal due to the potential cognitive and motor effects of CNS-active drugs.

METHODS

Our manuscript outlines the procedures for CNS clinical trials and highlights the key elements of study design, methodological considerations, and ethical frameworks. To achieve our objectives, we considered the official websites of regulatory authorities, the EQUATOR network, and recent publications in the field. The paper includes key elements such as criteria for subject selection, methods of evaluation, variable analysis, and statistical methodology approaches.

RESULTS

We want to furnish a concise and comprehensive guide tailored to individuals new to CNS clinical trials, providing foundational elements necessary for the design and execution of such trials. The manuscript seeks to outline sources of relevant materials and elucidate adaptability, particularly in instances where sponsors may be absent.

CONCLUSIONS

By meeting the needs of less-experienced researchers or those with limited resources, the intention is to facilitate an understanding of the intricate nature of the process and offer guidance on appropriately navigating its complexities. It is essential to note that this manuscript does not aim to be exhaustive but endeavors to serve as a structured checklist. Through its approach, the manuscript aspires to offer guidance and support to individuals navigating the challenges inherent in this intricate domain.

MPCD: A Multitask Graph Transformer for Molecular Property Prediction by Integrating Common and Domain Knowledge

Molecular property prediction with deep learning often employs self-supervised learning techniques to learn common knowledge through masked atom prediction. However, the common knowledge gained by masked atom prediction dramatically differs from the graph-level optimization objective of downstream tasks, which results in suboptimal problems. Particularly for properties with limited data, the failure to consider domain knowledge results in a direct search in an immense common space, rendering it infeasible to identify the global optimum. To address this, we propose MPCD, which enhances pretraining transferability by aligning the optimization objectives between pretraining and fine-tuning with domain knowledge. MPCD also leverages multitask learning to improve data utilization and model robustness. Technically, MPCD employs a relation-aware self-attention mechanism to capture molecules' local and global structures comprehensively. Extensive validation demonstrates that MPCD outperforms state-of-the-art methods for absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical prediction across various data sizes.

Analyzing the distribution of progression-free survival for combination therapies: A study of model-based translational predictive methods in oncology

Progression-free survival (PFS) is an important clinical metric in oncology and is typically illustrated and evaluated using a survival function. The survival function is often estimated post-hoc using the Kaplan-Meier estimator but more sophisticated techniques, such as population modeling using the nonlinear mixed-effects framework, also exist and are used for predictions. However, depending on the choice of population model PFS will follow different distributions both quantitatively and qualitatively. Hence the choice of model will also affect the predictions of the survival curves. In this paper, we analyze the distribution of PFS for a frequently used tumor growth inhibition model with and without drug-resistance and highlight the translational implications of this. Moreover, we explore and compare how the PFS distribution for combination therapy differs under the hypotheses of additive and independent-drug action. Furthermore, we calibrate the model to preclinical data and use a previously calibrated clinical model to show that our analytical conclusions are applicable to real-world setting. Finally, we demonstrate that independent-drug action can effectively describe the tumor dynamics of patient-derived xenografts (PDXs) given certain drug combinations.

The landscape of clinical trials research in inherited ophthalmic disease

OBJECTIVE

To describe the current status of clinical trials of genetic eye diseases with identified molecular targets for future areas of research.

METHOD

Data analysis of the clinical trials database on clinicaltrials.gov with keywords for eight common, genetically tractable inherited eye diseases and their common molecular targets was performed during the period from 20 March 2021 to 31 December 2023.

RESULTS

Two hundred and eighty-eight trials involving our keywords have been identified, excluding 25 (8.7%) trials which were unknown (verification expired with no update), 14 (4.9%) trials which were terminated early and 6(2.1%) trials which were withdrawn. In total there were 243 (84.4%) trials included. Out of the 243 trials, 120 trials were completed, 76 trials were active and still open to recruitment and 44 trials were active without any more recruitment on the way. There were only 32 (13.2%) trials with posted results.

CONCLUSIONS

A low percentage of results were posted for completed trials. However, current and future clinical trials in the genetic eye diseases with molecular targets identified, have a promising future. The results of these trials will enhance and allow a better understanding of the potential to develop treatments for these conditions.

Databases of ligand-binding pockets and protein-ligand interactions

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

CDCM: a correlation-dependent connectivity map approach to rapidly screen drugs during outbreaks of infectious diseases

In the context of the global damage caused by coronavirus disease 2019 (COVID-19) and the emergence of the monkeypox virus (MPXV) outbreak as a public health emergency of international concern, research into methods that can rapidly test potential therapeutics during an outbreak of a new infectious disease is urgently needed. Computational drug discovery is an effective way to solve such problems. The existence of various large open databases has mitigated the time and resource consumption of traditional drug development and improved the speed of drug discovery. However, the diversity of cell lines used in various databases remains limited, and previous drug discovery methods are ineffective for cross-cell prediction. In this study, we propose a correlation-dependent connectivity map (CDCM) to achieve cross-cell predictions of drug similarity. The CDCM mainly identifies drug-drug or disease-drug relationships from the perspective of gene networks by exploring the correlation changes between genes and identifying similarities in the effects of drugs or diseases on gene expression. We validated the CDCM on multiple datasets and found that it performed well for drug identification across cell lines. A comparison with the Connectivity Map revealed that our method was more stable and performed better across different cell lines. In the application of the CDCM to COVID-19 and MPXV data, the predictions of potential therapeutic compounds for COVID-19 were consistent with several previous studies, and most of the predicted drugs were found to be experimentally effective against MPXV. This result confirms the practical value of the CDCM. With the ability to predict across cell lines, the CDCM outperforms the Connectivity Map, and it has wider application prospects and a reduced cost of use.

Structure-based virtual screening of natural compounds in preventing skin senescence: The role of epigallocatechin gallate in protein kinase C alpha-specific inhibition against UV-induced photoaging

This study combines high-throughput screening and virtual molecular docking to identify natural compounds targeting PKC in skin aging. Go 6983, a PKC inhibitor, showed potent suppression of MMP-1 transcription. EGCG was one of the candidates that showed it could significantly lower UVB-induced MMP-1 expression in HaCaT cells, and it had a strong affinity for PKCα. Interestingly, EGCG is exclusively bound to PKCα, not the δ and ζ isoforms. Blocking PKCα did not elevate UVB-induced MMP-1 expression in HaCaT cells. In a model of human skin, EGCG stopped collagen breakdown and changes in epidermal thickness that were caused by UV light from the sun. This suggests that EGCG could be useful in dermatology and drug development. These findings highlight the role of structure-based screening in identifying candidate compounds with applications in the cosmetic, dermatological, preventive health, and pharmaceutical fields.

Medication development for AUD: A systematic review of clinical trial methodology

Refining clinical trial methodology has become increasingly important as study design is shown to influence treatment efficacy. To maximize the efficiency of randomized clinical trials (RCTs), researchers aim to establish standardized practices. The goal of this systematic review is to describe methodological practices of clinical trials for alcohol use disorder (AUD) over the past 40 years. To achieve this goal, a PubMed search was conducted in April 2023 for RCTs on AUD medications published between July 2018 through April 2023. Resulting studies were combined with a previous search from 1985 through 2018. Inclusion criteria for the RCT studies were: (1) a randomized controlled trial, (2) double or single blinded, (3) placebo or active control condition, (4) alcohol use as the primary endpoint, (5) 4 or more weeks of treatment, and (6) 12 or more weeks of follow-up. In total, methodological data from 139 RCTs representing 19 medications and spanning the past four decades were summarized. Results indicated that the most common medications tested were naltrexone (k = 42), acamprosate (k = 24), and baclofen (k = 11). On average, participants were 74% male and consumed 226 drinks per month pre-randomization. The median length of treatment was 12 weeks (IQR = 12-16; min = 4 max = 52) and the median follow-up duration was 12.5 weeks (IQR: 12-26; min = 7 max = 104). There were two broad domains of outcomes (i.e., abstinence and heavy drinking), with most studies featuring outcomes from both domains (k = 87; 63%). Reporting practices were summarized by decade, revealing an increased enrollment of females, better reporting of race and ethnicity data, and less studies requiring pre-trial abstinence. This review summarizes the current state of the literature on randomized clinical trials for AUD including effect sizes for individual studies and summaries of key methodological features across this representative set of clinical trials.

Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges

INTRODUCTION

Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead compound's pharmacodynamic, pharmacokinetic, and physiochemical properties. Scaffold hopping opens up fresh molecular territory beyond established patented chemical domains.

AREA COVERED

The authors present the scaffold hopping-based drug design strategies for dual inhibitory antitumor structural templates in this review. Minor modifications, structure rigidification and simplification (ring-closing and opening), and complete structural overhauls were the strategies employed by the medicinal chemist to generate a library of bifunctional inhibitors. In addition, the review presents an overview of the computational methods of scaffold hopping (software and programs) and organopalladium catalysis leveraged for the synthesis of templates designed via scaffold hopping.

EXPERT OPINION

The medicinal chemist has demonstrated remarkable prowess in furnishing dual inhibitory antitumor chemical architectures. Scaffold hopping-based drug design strategies have yielded a plethora of pharmacodynamically superior dual modulatory antitumor agents. An integrated approach involving computational advancements, synthetic methodology advancements, and conventional drug design strategies is required to increase the number of scaffold-hopping-assisted drug discovery campaigns.

SMCVdb: a database of experimental cellular toxicity information for drug candidate molecules

Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.

Promising tools for future drug discovery and development in antiarrhythmic therapy

Arrhythmia refers to irregularities in the rate and rhythm of the heart, with symptoms spanning from mild palpitations to life-threatening arrhythmias and sudden cardiac death (SCD). The complex molecular nature of arrhythmias complicates the selection of appropriate treatment. Current therapies involve the use of antiarrhythmic drugs (class I-IV) with limited efficacy and dangerous side effects and implantable pacemakers and cardioverter-defibrillators with hardware-related complications and inappropriate shocks. The number of novel antiarrhythmic drug in the development pipeline has decreased substantially during the last decade and underscores uncertainties regarding future developments in this field. Consequently, arrhythmia treatment poses significant challenges, prompting the need for alternative approaches. Remarkably, innovative drug discovery and development technologies show promise in helping advance antiarrhythmic therapies. Here, we review unique characteristics and the transformative potential of emerging technologies that offer unprecedented opportunities for transitioning from traditional antiarrhythmics to next-generation therapies. We assess stem cell technology, emphasizing the utility of innovative cell profiling using multi-omics, high-throughput screening, and advanced computational modeling in developing treatments tailored precisely to individual genetic and physiological profiles. We offer insights into gene therapy, peptide and peptibody approaches for drug delivery. We finally discuss potential strengths and weaknesses of such techniques in reducing adverse effects and enhancing overall treatment outcomes, leading to more effective, specific, and safer therapies. Altogether, this comprehensive overview introduces innovative avenues for personalized rhythm therapy, with particular emphasis on drug discovery, aiming to advance the arrhythmia treatment landscape and the prevention of SCD. Significance Statement Arrhythmias and sudden cardiac death account for 15-20% of deaths worldwide. However, current antiarrhythmic therapies are ineffective and with dangerous side effects. Here, we review the field of arrhythmia treatment underscoring the slow progress in advancing the cardiac rhythm therapy pipeline and the uncertainties regarding evolution of this field. We provide information on how emerging technological and experimental tools can help accelerate progress and address the limitations of antiarrhythmic drug discovery.

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

Myco-Biosynthesis of Silver Nanoparticles, Optimization, Characterization, and In Silico Anticancer Activities by Molecular Docking Approach against Hepatic and Breast Cancer

This study explored the green synthesis of silver nanoparticles (AgNPs) using the extracellular filtrate of Fusarium oxysporum as a reducing agent and evaluated their antitumor potential through in vitro and in silico approaches. The biosynthesis of AgNPs was monitored by visual observation of the color change and confirmed by UV-Vis spectroscopy, revealing a characteristic peak at 418 nm. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) analyses showed spherical nanoparticles ranging from 6.53 to 21.84 nm in size, with stable colloidal behavior and a negative zeta potential of -15.5 mV. Selected area electron diffraction (SAED) confirmed the crystalline nature of the AgNPs, whereas energy-dispersive X-ray (EDX) indicated the presence of elemental silver at 34.35%. A face-centered central composite design (FCCD) was employed to optimize the biosynthesis process, yielding a maximum AgNPs yield of 96.77 µg/mL under the optimized conditions. The antitumor efficacy of AgNPs against MCF-7 and HepG2 cancer cell lines was assessed, with IC50 values of 35.4 µg/mL and 7.6 µg/mL, respectively. Molecular docking revealed interactions between Ag metal and key amino acids of BCL-2 (B-cell lymphoma-2) and FGF19 (fibroblast growth factor 19), consistent with in vitro data. These findings highlight the potential of biologically derived AgNPs as promising therapeutic agents for cancer treatment and demonstrate the utility of these methods for understanding the reaction mechanisms and optimizing nanomaterial synthesis.

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Pharmacology of Aging: Drosophila as a Tool to Validate Drug Targets for Healthy Lifespan

Finding effective therapies to manage age-related conditions is an emerging public health challenge. Although disease-targeted treatments are important, a preventive approach focused on aging can be more efficient. Pharmacological targeting of aging-related processes can extend lifespan and improve health in animal models. However, drug development and translation are particularly challenging in geroscience. Preclinical studies have survival as a major endpoint for drug screening, which requires years of research in mammalian models. Shorter-lived invertebrates can be exploited to accelerate this process. In particular, the fruit fly Drosophila melanogaster allows the validation of new drug targets using precise genetic tools and proof-of-concept experiments on drugs impacting conserved aging processes. Screening for clinically approved drugs that act on aging-related targets may further accelerate translation and create new tools for aging research. To date, 31 drugs used in clinical practice have been shown to extend the lifespan of flies. Here, we describe recent advances in the pharmacology of aging, focusing on Drosophila as a tool to repurpose these drugs and study age-related processes.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Shared etiology of Mendelian and complex disease supports drug discovery

BACKGROUND

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

METHODS

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

RESULTS

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

CONCLUSIONS

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

C ucumis melo compounds: A new avenue for ALR-2 inhibition in diabetes mellitus

Diabetes mellitus (DM) is a prominent contributor to morbidity and mortality in developed nations, primarily attributable to vascular complications such as atherothrombosis occurring in the coronary arteries. Aldose reductase (ALR2), the main enzyme in the polyol pathway, catalyzes the conversion of glucose to sorbitol, leading to a significant buildup of reactive oxygen species in different tissues. It is therefore a prime candidate for therapeutic targeting, and extensive study is currently underway to discover novel natural compounds that can inhibit it. Cucumis melo (C. melo) has a long history as a lipid-lowering ethanopharmaceutical plant. In this study, compounds derived from C. melo were computationally evaluated as possible lead candidates. Various computational filtering methods were employed to assess the drug-like properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles of the compounds. The compounds were subsequently addressed to analysis of their interactions, molecular docking, and molecular dynamics simulation studies. When compared to the conventional therapeutic compounds, three compounds exhibited enhanced binding affinity and intra-molecular residue interactions, resulting in increased stability and specificity. Consequently, four potent inhibitors, namely PubChem CIDs 119205, 65373, 6184, and 332427, have been identified. These inhibitors exhibit promising potential as pharmacological targets for the advancement of novel ALR-2 inhibitors.

Aurora kinase as a putative target to tick control

Aurora kinases (AURK) play a central role in controlling cell cycle in a wide range of organisms. They belong to the family of serine-threonine kinase proteins. Their role in the cell cycle includes, among others, the entry into mitosis, maturation of the centrosome and formation of the mitotic spindle. In mammals, 3 isoforms have been described: A, B and C, which are distinguished mainly by their function throughout the cell cycle. Two aurora kinase coding sequences have been identified in the transcriptome of the cattle tick Rhipicephalus microplus (Rm-AURKA and Rm-AURKB) containing the aurora kinase-specific domain. For both isoforms, the highest number of AURK coding transcripts is found in ovaries. Based on deduced amino acid sequences, it was possible to identify non-conserved threonine residues which are essential to AURK functions in vertebrates and which are not present in R. microplus sequences. A pan AURK inhibitor (CCT137690) caused cell viability decline in the BME26 tick embryonic cell line. In silico docking assay showed an interaction between Aurora kinase and CCT137690 with exclusive interaction sites in Rm-AURKA. The characterization of exclusive regions of the enzyme will enable new studies aimed at promoting species-specific enzymatic inhibition in ectoparasites.

Deconvolving Passive and Active Targeting of Liposomes Bearing LDL Receptor Binding Peptides Using the Zebrafish Embryo Model

Improving target versus off-target ratio in nanomedicine remains a major challenge for increasing drug bioavailability and reducing toxicity. Active targeting using ligands on nanoparticle surfaces is a key approach but has limited clinical success. A potential issue is the integration of targeting ligands also changes the physicochemical properties of nanoparticles (passive targeting). Direct studies to understand the mechanisms of active targeting and off-targeting in vivo are limited by the lack of suitable tools. Here, the biodistribution of a representative active targeting liposome is analyzed, modified with an apolipoprotein E (ApoE) peptide that binds to the low-density lipoprotein receptor (LDLR), using zebrafish embryos. The ApoE liposomes demonstrated the expected liver targeting effect but also accumulated in the kidney glomerulus. The ldlra-/- zebrafish is developed to explore the LDLR-specificity of ApoE liposomes. Interestingly, liver targeting depends on the LDLR-specific interaction, while glomerular accumulation is independent of LDLR and peptide sequence. It is found that cationic charges of peptides and the size of liposomes govern glomerular targeting. Increasing the size of ApoE liposomes can avoid this off-targeting. Taken together, the study shows the potential of the zebrafish embryo model for understanding active and passive targeting mechanisms, that can be used to optimize the design of nanoparticles.

Optimization and evaluation of modified release solid dosage forms using artificial neural network

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

Secondary metabolite profile of Streptomyces spp. changes when grown with the sub-lethal concentration of silver nanoparticles: possible implication in novel compound discovery

The decline of new antibiotics and the emergence of multidrug resistance in pathogens necessitates a revisit of strategies used for lead compound discovery. This study proposes to induce the production of bioactive compounds with sub-lethal concentrations of silver nanoparticles (Ag-NPs). A total of Forty-two Actinobacteria isolates from four Saudi soil samples were grown with and without sub-lethal concentration of Ag-NPs (50 µg ml-1). The spent broth grown with Ag-NPs, or without Ag-NPs were screened for antimicrobial activity against four bacteria. Interestingly, out of 42 strains, broths of three strains grown with sub-lethal concentration of Ag-NPs exhibit antimicrobial activity against Staphylococcus aureus and Micrococcus luteus. Among these, two strains S4-4 and S4-21 identified as Streptomyces labedae and Streptomyces tirandamycinicus based on 16S rRNA gene sequence were selected for detailed study. The change in the secondary metabolites profile in the presence of Ag-NPs was evaluated using GC-MS and LC-MS analyses. Butanol extracts of spent broth grown with Ag-NPs exhibit strong antimicrobial activity against M. luteus and S. aureus. While the extracts of the controls with the same concentration of Ag-NPs do not show any activity. GC-analysis revealed a clear change in the secondary metabolite profile when grown with Ag-NPs. Similarly, the LC-MS patterns also differ significantly. Results of this study, strongly suggest that sub-lethal concentrations of Ag-NPs influence the production of secondary metabolites by Streptomyces. Besides, LC-MS results identified possible secondary metabolites, associated with oxidative stress and antimicrobial activities. This strategy can be used to possibly induce cryptic biosynthetic gene clusters for the discovery of new lead compounds.

CETP and SGLT2 inhibitor combination therapy increases glycemic control: a 2x2 factorial Mendelian randomization analysis

Introduction

Cholesteryl ester transfer protein (CETP) inhibitors, initially developed for treating hyperlipidemia, have shown promise in reducing the risk of new-onset diabetes during clinical trials. This positions CETP inhibitors as potential candidates for repurposing in metabolic disease treatment. Given their oral administration, they could complement existing oral medications like sodium-glucose cotransporter 2 (SGLT2) inhibitors, potentially delaying the need for injectable therapies such as insulin.

Methods

We conducted a 2x2 factorial Mendelian Randomization analysis involving 233,765 participants from the UK Biobank. This study aimed to evaluate whether simultaneous genetic inhibition of CETP and SGLT2 enhances glycemic control compared to inhibiting each separately.

Results

Our findings indicate that dual genetic inhibition of CETP and SGLT2 significantly reduces glycated hemoglobin levels compared to controls and single-agent inhibition. Additionally, the combined inhibition is linked to a lower incidence of diabetes compared to both the control group and SGLT2 inhibition alone.

Discussion

These results suggest that combining CETP and SGLT2 inhibitor therapies may offer superior glycemic control over SGLT2 inhibitors alone. Future clinical trials should investigate the potential of repurposing CETP inhibitors for metabolic disease treatment, providing an oral therapeutic option that could benefit high-risk patients before they require injectable therapies like insulin or glucagon-like peptide-1 (GLP-1) receptor agonists.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Cell viability imaging in tumor spheroids via DNA binding of a ruthenium(II) light-switch complex

The famous ''light-switch'' ruthenium complex [Ru(bpy)2(dppz)](PF6)2 (1) has been long known for its DNA binding properties in vitro. However, the biological utility of this compound has been hampered by its poor cellular uptake in living cells. Here we report a bioimaging application of 1 as cell viability probe in both 2D cells monolayer and 3D multi-cellular tumor spheroids of various human cancer cell lines (U87, HepG2, A549). When compared to propidium iodide, a routinely used cell viability probe, 1 was found to enhance the staining of dead cells in particular in tumor spheroids. 1 has high photostability, longer Stokes shift, and displays lower cytotoxicity compared to propidium iodide, which is a known carcinogenic. Finally, 1 was also found to displace the classical DNA binding dye Hoechst in dead cells, which makes it a promising dye for time-dependent imaging of dead cells in cell cultures, including multi-cellular tumor spheroids.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

LigandDiff: de Novo Ligand Design for 3D Transition Metal Complexes with Diffusion Models

Transition metal complexes are a class of compounds with varied and versatile properties, making them of great technological importance. Their applications cover a wide range of fields, either as metallodrugs in medicine or as materials, catalysts, batteries, solar cells, etc. The demand for the novel design of transition metal complexes with new properties remains of great interest. However, the traditional high-throughput screening approach is inherently expensive and laborious since it depends on human expertise. Here, we present LigandDiff, a generative model for the de novo design of novel transition metal complexes. Unlike the existing methods that simply extract and combine ligands with the metal to get new complexes, LigandDiff aims at designing configurationally novel ligands from scratch, which opens new pathways for the discovery of organometallic complexes. Moreover, it overcomes the limitations of current methods, where the diversity of new complexes highly relies on the diversity of available ligands, while LigandDiff can design numerous novel ligands without human intervention. Our results indicate that LigandDiff designs unique and novel ligands under different contexts, and these generated ligands are synthetically accessible. Moreover, LigandDiff shows good transferability by generating successful ligands for any transition metal complex.

Protein Structure Inspired Drug Discovery

Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.

Historical Perspective and Current Trends in Anticancer Drug Development

Cancer is considered one of the leading causes of death in the 21st century. The intensive search for new anticancer drugs has been actively pursued by chemists and pharmacologists for decades, focusing either on the isolation of compounds with cytotoxic properties from plants or on screening thousands of synthetic molecules. Compounds that could potentially become candidates for new anticancer drugs must have the ability to inhibit proliferation and/or induce apoptosis in cancer cells without causing too much damage to normal cells. Some anticancer compounds were discovered by accident, others as a result of long-term research. In this review, we have presented a brief history of the development of the most important groups of anticancer drugs, pointing to the fact that they all have many side effects.

Assessing patient risk, benefit, and outcomes in drug development: A decade of ramucirumab clinical trials

OBJECTIVE

This study aims to evaluate published clinical trials of ramucirumab to assess the risk/benefit profile and burden over time for patients.

BACKGROUND

The burden of oncologic drug development on patients paired with increasing clinical trial failure rates emphasizes the need for reform of drug development. Identifying and addressing patterns of excess burden can guide policy, ensure evidence-based protections for trial participants, and improve medical decision-making.

METHODS

On May 25, 2023 a literature search was performed on Pubmed/MEDLINE, Embase, Cochrane CENTRAL, and ClinicalTrials.gov for clinical trials using ramucirumab as monotherapy or in combination with other interventions for cancer treatment. Authors screened titles and abstracts for potential inclusion in a masked, duplicate fashion. Following data screening, data was extracted in a masked, duplicate fashion. Trials were classified as positive when meeting their primary endpoint and safety, negative or indeterminate.

RESULTS

Ramucirumab was initially approved for gastric cancer but has since been tested in 20 cancers outside of its FDA approved indications. In our analysis of ramucirumab trials, there were a total of 10,936 participants and 10,303 adverse events reported. Gains in overall survival and progression-free survival for patients were 1.5 and 1.2 months, respectively. FDA-approved indications have reported more positive outcomes in comparison to off-label indications.

CONCLUSION

We found that FDA-approved indications for ramucirumab had better efficacy outcomes than non-approved indications. However, a concerning number of adverse events were observed across all trials assessed. Participants in ramucirumab randomized controlled trials saw meager gains in overall survival when evaluated against a comparison group. Clinicians should carefully weigh the risks associated with ramucirumab therapy given its toxicity burden and poor survival gains.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

Shared etiology of Mendelian and complex disease supports drug discovery

Background

Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.

Methods

In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.

Results

Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.

Conclusions

Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations

De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Flavonoids: A treasure house of prospective pharmacological potentials

Flavonoids are organic compounds characterized by a range of phenolic structures, which are abundantly present in various natural sources such as fruits, vegetables, cereals, bark, roots, stems, flowers, tea, and wine. The health advantages of these natural substances are renowned, and initiatives are being taken to extract the flavonoids. Apigenin, galangin, hesperetin, kaempferol, myricetin, naringenin, and quercetin are the seven most common compounds belonging to this class. A thorough analysis of bibliographic records from reliable sources including Google Scholar, Web of Science, PubMed, ScienceDirect, MEDLINE, and others was done to learn more about the biological activities of these flavonoids. These flavonoids appear to have promising anti-diabetic, anti-inflammatory, antibacterial, antioxidant, antiviral, cytotoxic, and lipid-lowering activities, according to evidence from in vitro, in vivo, and clinical research. The review contains recent trends, therapeutical interventions, and futuristic aspects of flavonoids to treat several diseases like diabetes, inflammation, bacterial and viral infections, cancers, and cardiovascular diseases. However, this manuscript should be handy in future drug discovery. Despite these encouraging findings, a notable gap exists in clinical research, hindering a comprehensive understanding of the effects of flavonoids at both high and low concentrations on human health. Future investigations should prioritize exploring bioavailability, given the potential for high inter-individual variation. As a starting point for further study on these flavonoids, this review paper may promote identifying and creating innovative therapeutic uses.

Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine

Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.

Antimalarial potential, LC-MS secondary metabolite profiling and computational studies of Zingiber officinale

Malaria is among the top-ranked parasitic diseases that pose a threat to the existence of the human race. This study evaluated the antimalarial effect of the rhizome of Zingiber officinale in infected mice, performed secondary metabolite profiling and detailed computational antimalarial evaluation through molecular docking, molecular dynamics (MD) simulation and density functional theory methods. The antimalarial potential of Z. officinale was performed using the in vivo chemosuppressive model; secondary metabolite profiling was carried out using liquid chromatography-mass spectrometry (LC-MS). Molecular docking was performed with Autodock Vina while the MD simulation was performed with Schrodinger desmond suite for 100 ns and DFT calculations with B3LYP (6-31G) basis set. The extract showed 64% parasitaemia suppression, with a dose-dependent increase in activity up to 200 mg/kg. The chemical profiling of the extract tentatively identified eight phytochemicals. The molecular docking studies with plasmepsin II and Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS) identified gingerenone A as the hit molecule, and MMGBSA values corroborate the binding energies obtained. The electronic parameters of gingerenone A revealed its significant antimalarial potential. The antimalarial activity elicited by the extract of Z. officinale and the bioactive chemical constituent supports its usage in ethnomedicine.Communicated by Ramaswamy H. Sarma.

Analysis of the first ten years of FDA's rare pediatric disease priority review voucher program: designations, diseases, and drug development

BACKGROUND

The Rare Pediatric Disease (RPD) Priority Review Voucher (PRV) Program was enacted in 2012 to support the development of new products for children. Prior to requesting a voucher, applicants can request RPD designation, which confirms their product treats or prevents a rare disease in which the serious manifestations primarily affect children. This study describes the trends and characteristics of these designations. Details of RPD designations are not publicly disclosable; this research represents the first analysis of the RPD designation component of the program.

RESULTS

We used an internal US Food and Drug Administration database to analyze all RPD designations between 2013 and 2022. Multiple characteristics were analyzed, including the diseases targeted by RPD designation, whether the product targeted a neonatal disease, product type (drug/biologic), and the level of evidence (preclinical/clinical) to support designation. There were 569 RPD designations during the study period. The top therapeutic areas were neurology (26%, n = 149), metabolism (23%, n = 131), oncology (18%, n = 105). The top diseases targeted by RPD designation were Duchenne muscular dystrophy, neuroblastoma, and sickle cell disease. Neonatology products represented 6% (n = 33), over half were for drug products and 38% were supported by clinical data.

CONCLUSIONS

The RPD PRV program was created to encourage development of new products for children. The results of this study establish that a wide range of diseases have seen development-from rare pediatric cancers to rare genetic disorders. Continued support of product development for children with rare diseases is needed to find treatments for all children with unmet needs.

The role of early-phase trials and real-world evidence in drug development

Phase 3 randomized controlled trials (RCTs), while the gold standard for treatment efficacy and safety, are not always feasible, are expensive, can be prolonged and can be limited in generalizability. Other under-recognized sources of evidence can also help advance drug development. Basic science, proof-of-concept studies and early-phase RCTs can provide evidence regarding the potential for clinical benefit. Real-world evidence generated from registries or observational datasets can provide insights into the treatment of rare diseases that often pose a challenge for trial recruitment. Pragmatic trials embedded in healthcare systems can assess the treatment effects in clinical settings among patient populations sometimes excluded from trials. This Perspective discusses potential sources of evidence that may be used to complement explanatory phase 3 RCTs and to speed the development of new cardiovascular medications. Content is derived from the 19th Global Cardiovascular Clinical Trialists meeting (December 2022), involving clinical trialists, patients, clinicians, regulators, funders and industry representatives.

Precision Deuteration in Search of Anticancer Agents: Approaches to Cancer Drug Discovery

Cancer chemotherapy has been shifted from conventional cytotoxic drug therapy to selective and target-specific therapy after the findings about DNA changes and proteins that are responsible for cancer. A large number of newer drugs were discovered as targeted therapy for particular types of neoplastic disease. The initial discovery includes the development of the first in the category, imatinib, a Bcr-Abl tyrosine kinase inhibitor (TKI) for the treatment of chronic myelocytic leukemia in 2001. But the joy did not last for long as the drug developed a point mutation within the ABL1 kinase domain of BCR-ABL1, which subsequently led to the discovery of many other TKIs. Resistance was observed for newer TKIs a few years after their launching, but the use of TKIs in life-threatening cancer therapy is considered as far better compared with the risks of disease because of its target specificity and hence less toxicity. In search of a better anticancer agent, the physiochemical properties of the lead molecule have been modified for its efficacy toward disease and delay in the development of resistance. Deuteration in the drug molecule is one of such modifications that alter the pharmacokinetic properties, generally its metabolism, as compared with its pharmacodynamic effects. Precision deuteration in many anticancer drugs has been carried out to search for better drugs for cancer. In this review, the majority of anticancer drugs and molecules for which deuteration was applied to get better anticancer molecules were discussed. This review will provide a complete guide about the benefits of deuteration in cancer chemotherapy.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

The use of artificial intelligence in the treatment of rare diseases: A scoping review

With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases (n = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used (n = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.