Importance of scientific collaboration in contemporary drug discovery and development: a detailed network analysis

Analyzing collaboration dynamics in new drug R&D: A case study of two lipid-lowering drugs

Introduction

The advancements in biotechnology have ushered in a new age for drug development, characterized by increased collaborative efforts. Academic institutions, pharmaceutical firms, hospitals, foundations, and various other entities across different sectors are now joining forces more frequently to accelerate new drug innovation. However, there remains a limited understanding of how scientific and technological advancements are influencing these research collaborations.

Methods

In this study, the development of two types of lipid-lowering drugs served as case studies. A detailed network analysis was performed at the levels of authors, institutions, and countries to quantify the evolutionary trends in research collaboration.

Results

In the clinical research segment of the academic chain, papers resulting from collaborations tend to receive a higher citation count compared to other areas. However, there were notably fewer collaborative connections between authors transitioning from basic to developmental research and beyond. Collaboration models involving universities with enterprises, hospitals, or both are becoming more prevalent in biologics R&D. These models demonstrate effects of similarity and proximity. Additionally, there has been an increase in the involvement of developing countries in the research and development of new biologic drugs on a national and regional scale.

Conclusion

New drug R&D research collaboration patterns evolve spontaneously with productivity updates. In the future, it is necessary to enhance the involvement of pharmaceutical companies in the basic research phase of new drug development, continuously strengthen the relationships across all segments of the academic chain, and thoroughly boost the efficiency of transforming new drug R&D into practical applications.

Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance

Most cancer-related deaths result from drug-resistant disease(1,2). However, cancer drug resistance is not a primary focus in drug development. Effectively mitigating and treating drug-resistant cancer will require advancements in multiple fields, including early detection, drug discovery, and our fundamental understanding of cancer biology. Therefore, successfully tackling drug resistance requires an increasingly multidisciplinary approach. A recent workshop on cancer drug resistance, jointly organised by Cancer Research UK, the Rosetrees Trust, and the UKRI-funded Physics of Life Network, brought together experts in cell biology, physical sciences, computational biology, drug discovery, and clinicians to focus on these key challenges and devise interdisciplinary approaches to address them. In this perspective, we review the outcomes of the workshop and highlight unanswered research questions. We outline the emerging hallmarks of drug resistance and discuss lessons from the COVID-19 pandemic and antimicrobial resistance that could help accelerate information sharing and timely adoption of research discoveries into the clinic. We envisage that initiatives that drive greater interdisciplinarity will yield rich dividends in developing new ways to better detect, monitor, and treat drug resistance, thereby improving treatment outcomes for cancer patients.

Advanced Hydrogel Systems for Local Anesthetic Delivery: Toward Prolonged and Targeted Pain Relief

Local anesthetics (LAs) have been indispensable in clinical pain management, yet their limitations, such as short duration of action and systemic toxicity, necessitate improved delivery strategies. Hydrogels, with their biocompatibility, tunable properties, and ability to modulate drug release, have been extensively explored as platforms for enhancing LA efficacy and safety. This narrative review explores the historical development of LAs, their physicochemical properties, and clinical applications, providing a foundation for understanding the integration of hydrogels in anesthetic delivery. Advances in thermoresponsive, stimuli-responsive, and multifunctional hydrogels have demonstrated significant potential in prolonging analgesia and reducing systemic exposure in preclinical studies, while early clinical findings highlight the feasibility of thermoresponsive hydrogel formulations. Despite these advancements, challenges such as burst release, mechanical instability, and regulatory considerations remain critical barriers to clinical translation. Emerging innovations, including nanocomposite hydrogels, biofunctionalized matrices, and smart materials, offer potential solutions to these limitations. Future research should focus on optimizing hydrogel formulations, expanding clinical validation, and integrating advanced fabrication technologies such as 3D printing and artificial intelligence-driven design to enhance personalized pain management. By bridging materials science and anesthetic pharmacology, this review provides a comprehensive perspective on current trends and future directions in hydrogel-based LA delivery systems.

Targeting bacterial transcription factors for infection control: opportunities and challenges

The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.

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.

Single-cell sequencing reveals that specnuezhenide protects against osteoporosis via activation of METTL3 in LEPR+ BMSCs

BACKGROUND

Osteoporosis (OP) has garnered significant attention due to its substantial morbidity and mortality rates, imposing considerable health burdens on societies worldwide. However, the molecular mechanisms underlying osteoporosis pathogenesis remain largely elusive, and the available therapeutic interventions are limited. Therefore, there is an urgent need for innovative strategies in the treatment of osteoporosis.

PURPOSE

The primary objective of this study was to elucidate the molecular mechanisms underlying osteoporosis pathogenesis using single-cell RNA sequencing (scRNA-seq), thereby proposing novel therapeutic agents.

METHODS

The mice osteoporosis model was established through bilateral ovariectomy. Micro-computed tomography (μCT) and hematoxylin and eosin (H&E) staining were employed to assess the pathogenesis of osteoporosis. scRNA-seq was utilized to identify and analyze distinct molecular mechanisms and sub-clusters. Gradient dilution analysis was used to obtain specific sub-clusters, which were further validated by immunofluorescence staining and flow cytometry analysis. Molecular docking and cellular thermal shift assay (CETSA) were applied for screening potential agents in the TCMSPs database. Alkaline phosphatase (ALP) activity and alizarin red S (ARS) staining were performed to evaluate the osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs). Osteogenic organoids analysis was employed to assess the proliferation and sphere-forming ability of BMSCs. Quantitative real-time PCR (qRT-PCR) and western blot analysis were conducted to investigate signaling pathways. Wound healing assay and tube formation analysis were employed to evaluate the angiogenesis of endothelial cells.

RESULTS

The scRNA-seq analysis revealed the crucial role of LEPR+ BMSCs in the pathogenesis of osteoporosis, which was confirmed by immunofluorescence staining of the epiphysis. Subsequently, the LEPR+ BMSCs were obtained by gradient dilution analysis and identified by immunofluorescence staining and flow cytometry. Accordingly, specnuezhenide (Spe) was screened and identified as a potential compound targeting METTL3 from the TCMSPs database. Spe promoted bone formation as evidenced by μ-CT, and H&E analysis. Additionally, Spe enhanced the osteogenic capacity of LEPR+ BMSCs through ALP and ARS assay. Notably, METTL3 pharmacological inhibitors S-Adenosylhomocysteine (SAH) attenuated the aforementioned osteo-protective effects of Spe. Particularly, Spe enhanced the LEPR+ BMSCs-dependent angiogenesis through the secretion of SLIT3, which was abolished by SAH in LEPR+ BMSCs.

CONCLUSION

Collectively, these findings suggest that Spe could enhance the osteogenic potential of LEPR+ BMSCs and promote LEPR+ BMSCs-dependent angiogenesis by activating METTL3 in LEPR+ BMSCs, indicating its potential as an ideal therapeutic agent for clinical treatment of osteoporosis.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Social network analysis of a decade-long collaborative innovation network between hospitals and the biomedical industry in China

Collaborative innovation between hospitals and biomedical enterprises is crucial for ensuring breakthroughs in their development. This study explores the structural characteristics and examines the main roles of associated key actors of collaborative innovation between hospitals and biomedical enterprises in China. Using the jointly owned patent data within the country's healthcare industry, a decade-long collaborative innovation network between hospitals and biomedical enterprises in China was established and analyzed through social network analysis. The results revealed that the overall levels of collaborative innovation network density, collaborative frequency, and network connectivity were significantly low, especially in less-developed regions. In terms of actors with higher degree centrality, hospitals accounted for the majority, whereas a biomedical enterprise in Shenzhen had the highest degree centrality. Organizations in underdeveloped and northwest regions and small players were more likely to implement collaborative innovation. In conclusion, a collaborative innovation network between hospitals and biomedical enterprises in China demonstrated high dispersion and poor development levels. Stimulating organizations' initiatives for collaborative innovation may enhance quality and quantity of such innovation. Policy support and economic investments, strategic collaborative help, and resource and partnership optimization, especially for small players and in less-developed and northwest regions, should be encouraged to enhance collaborative innovation between hospitals and the biomedical industry in China and other similar countries or regions.

Exploring regulatory flexibility to create novel incentives to optimize drug discovery

Efforts by governments, firms, and patients to deliver pioneering drugs for critical health needs face a challenge of diminishing efficiency in developing those medicines. While multi-sectoral collaborations involving firms, researchers, patients, and policymakers are widely recognized as crucial for countering this decline, existing incentives to engage in drug development predominantly target drug manufacturers and thereby do little to stimulate collaborative innovation. In this mini review, we consider the unexplored potential within pharmaceutical regulations to create novel incentives to encourage a diverse set of actors from the public and private spheres to engage in the kind of collaborative knowledge exchange requisite for fostering enhanced innovation in early drug development.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .

International comparison of pharmaceutical industry payment disclosures in the UK and Japan: implications for self-regulation, public regulation, and transparency

BACKGROUND

Self-regulation of payment disclosure by pharmaceutical industry trade groups is a major global approach to increasing transparency of financial relationships between drug companies and healthcare professionals and organisations. Nevertheless, little is known about the relative strengths and weaknesses of self-regulation across countries, especially beyond Europe. To address this gap in research and stimulate international policy learning, we compare the UK and Japan, the likely strongest cases of self-regulation of payment disclosure in Europe and Asia, across three dimensions of transparency: disclosure rules, practices, and data.

RESULTS

The UK and Japanese self-regulation of payment disclosure had shared as well unique strengths and weaknesses. The UK and Japanese pharmaceutical industry trade groups declared transparency as the primary goal of payment disclosure, without, however, explaining the link between the two. The rules of payment disclosure in each country provided more insight into some payments but not others. Both trade groups did not reveal the recipients of certain payments by default, and the UK trade group also made the disclosure of some payments conditional on recipient consent. Drug company disclosure practices were more transparent in the UK, allowing for greater availability and accessibility of payment data and insight into underreporting or misreporting of payments by companies. Nevertheless, the share of payments made to named recipients was three times higher in Japan than in the UK, indicating higher transparency of disclosure data.

CONCLUSIONS

The UK and Japan performed differently across the three dimensions of transparency, suggesting that any comprehensive analysis of self-regulation of payment disclosure must triangulate analysis of disclosure rules, practices, and data. We found limited evidence to support key claims regarding the strengths of self-regulation, while often finding it inferior to public regulation of payment disclosure. We suggest how the self-regulation of payment disclosure in each country can be enhanced and, in the long run, replaced by public regulation to strengthen the industry's accountability to the public.

Multimodal single-cell omics analysis identifies epithelium-immune cell interactions and immune vulnerability associated with sex differences in COVID-19

Sex differences in the susceptibility of SARS-CoV-2 infection and severity have been controversial, and the underlying mechanisms of COVID-19 in a sex-specific manner remain understudied. Here we inspected sex differences in SARS-CoV-2 infection, hospitalization, admission to the intensive care unit (ICU), sera inflammatory biomarker profiling, and single-cell RNA-sequencing (scRNA-seq) profiles across nasal, bronchoalveolar lavage fluid (BALF), and peripheral blood mononuclear cells (PBMCs) from COVID-19 patients with varying degrees of disease severities. Our propensity score-matching observations revealed that male individuals have a 29% elevated likelihood of SARS-CoV-2 positivity, with a hazard ratio (HR) 1.32 (95% confidence interval [CI] 1.18–1.48) for hospitalization and HR 1.51 (95% CI 1.24–1.84) for admission to ICU. Sera from male patients at hospital admission had elevated neutrophil–lymphocyte ratio and elevated expression of inflammatory markers (C-reactive protein and procalcitonin). We found that SARS-CoV-2 entry factors, including ACE2, TMPRSS2, FURIN, and NRP1, have elevated expression in nasal squamous cells from male individuals with moderate and severe COVID-19. We observed male-biased transcriptional activation in SARS-CoV-2-infected macrophages from BALF and sputum samples, which offers potential molecular mechanism for sex-biased susceptibility to viral infection. Cell–cell interaction network analysis reveals potential epithelium–immune cell interactions and immune vulnerability underlying male-elevated disease severity and mortality in COVID-19. Mechanistically, monocyte-elevated expression of Toll-like receptor 7 (TLR7) and Bruton tyrosine kinase (BTK) is associated with severe outcomes in males with COVID-19. In summary, these findings provide basis to decipher immune responses underlying sex differences and designing sex-specific targeted interventions and patient care for COVID-19.