Landscape Of Drug Development Research Articles

Dose Optimization in Oncology Drug Development: An International Consortium for Innovation and Quality in Pharmaceutical Development White Paper.

The landscape of oncology drug development has witnessed remarkable advancements over the last few decades, significantly improving clinical outcomes and quality of life for patients with cancer. Project Optimus, introduced by the U.S. Food and Drug Administration, stands as a groundbreaking endeavor to reform dose selection of oncology drugs, presenting both opportunities and challenges for the field. To address complex dose optimization challenges, an Oncology Dose Optimization IQ Working Group was created to characterize current practices, provide recommendations for improvement, develop a clinical toolkit, and engage Health Authorities. Historically, dose selection for cytotoxic chemotherapeutics has focused on the maximum tolerated dose, a paradigm that is less relevant for targeted therapies and new treatment modalities. A survey conducted by this group gathered insights from member companies regarding industry practices in oncology dose optimization. Given oncology drug development is a complex effort with multidimensional optimization and high failure rates due to lack of clinically relevant efficacy, this Working Group advocates for a case-by-case approach to inform the timing, specific quantitative targets, and strategies for dose optimization, depending on factors such as disease characteristics, patient population, mechanism of action, including associated resistance mechanisms, and therapeutic index. This white paper highlights the evolving nature of oncology dose optimization, the impact of Project Optimus, and the need for a tailored and evidence-based approach to optimize oncology drug dosing regimens effectively.

Drug Classification and Repurposing Using Decision Tree Algorithm and Data Analysis

Abstract: Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is undergoing transformation, fueled by innovative strategies that hold the promise of revolutionizing healthcare solutions. This research paper explores drug classification and repurposing through the utilization of decision tree algorithms and data analysis. Specifically, the aim is to classify drugs into three distinct types: Drug X which is most used drugs, Drug Y which is less frequently used drugs, and Drug C includes drugs like narcotics. Decision tree algorithms are employed to discern the defining attributes that categorize drugs into these types. By analyzing comprehensive datasets encompassing drug properties, interactions, and clinical outcomes, the study harnesses decision tree models to predict drug classifications accurately. This approach holds the potential to accelerate drug discovery, optimize treatment strategies, and contribute to more efficient healthcare solutions. The proposed algorithm is compared with both Naïve Bayes and K - Nearest Neighbors algorithms to prove it is more accurate. Ultimately, the significance of this paper transcends the boundaries of conventional drug development paradigms and to overcome the problem of shortage of drugs.

Peptide Drugs: Current Status and Treatment for the Treatment of Various Diseases.

Peptides represent a class of natural molecules with diverse physiological functions, including hormone regulation, neurotransmission, and immune modulation. In recent years, peptide-based therapeutics have gained significant attention in pharmaceutical research and development due to their high specificity, efficacy, and relatively low toxicity. This review provides an overview of the current landscape of peptide drug development, highlighting the challenges faced in their formulation and delivery and the innovative strategies employed to overcome these hurdles. The review explores the wide range of applications of peptide drugs in treating various diseases, including HIV, multiple sclerosis, osteoporosis, chronic pain, diabetes, and cancer. Examples of FDA-approved peptide drugs and ongoing clinical trials are presented, showcasing the continuous advancements in peptide-based therapeutics across different therapeutic areas. This review underscores the promising potential of peptide drugs as targeted and effective treatments for a multitude of medical conditions, offering improved therapeutic outcomes and enhanced patient care.

Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.

The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development of novel RNA-targeted therapeutics. Consequently, the determination of RNA-small molecule binding affinity is a critical undertaking in the landscape of RNA-targeted drug discovery and development. Nevertheless, to date, only one computational method for RNA-small molecule binding affinity prediction has been proposed. The prediction of RNA-small molecule binding affinity remains a significant challenge. The development of a computational model is deemed essential to effectively extract relevant features and predict RNA-small molecule binding affinity accurately. In this study, we introduced RLaffinity, a novel deep learning model designed for the prediction of RNA-small molecule binding affinity based on 3D structures. RLaffinity integrated information from RNA pockets and small molecules, utilizing a 3D convolutional neural network (3D-CNN) coupled with a contrastive learning-based self-supervised pre-training model. To the best of our knowledge, RLaffinity was the first deep learning based method for the prediction of RNA-small molecule binding affinity. Our experimental results exhibited RLaffinity's superior performance compared to baseline methods, revealed by all metrics. The efficacy of RLaffinity underscores the capability of 3D-CNN to accurately extract both global pocket information and local neighbor nucleotide information within RNAs. Notably, the integration of a self-supervised pre-training model significantly enhanced predictive performance. Ultimately, RLaffinity was also proved as a potential tool for RNA-targeted drugs virtual screening. https://github.com/SaisaiSun/RLaffinity.

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development.

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Precise cuts for tailoring chromene-phenyl COX inhibitors with Ligand Designer

Cyclooxygenases 1 and 2 (COX-1/2) are enzymes renowned for inducing inflammatory responses through the production of prostaglandins. Thus, the development of COX inhibitors has been a promising approach for identifying compounds with anti-inflammatory potential. In this study, we designed 27 new compounds (1–27) based on the structure of a previously known COX inhibitor, using the Ligand Designer tool. Our aim was to improve the affinity of the compounds with COX enzymes by inducing interactions with residue Arg120 while retaining the good π-π stacking interactions of the chromene-phenyl scaffold. Through screening based on ligand-binding free energy defined by molecular docking simulations and MM/GBSA technique, compounds 9 and 10 were identified as having the highest ability to inhibit COX proteins. The binding affinities of the two compounds with COX-1/2 were superior to those of the original NAI10 compound and the reference drug indomethacin. Our virtual screening suggests that compounds 9 and 10 have a strong ability to inhibit COX-1/2 and thus could be promising candidates for further anti-inflammatory drug studies. In essence, our study underscores the pivotal role of the N-aryl iminocoumarin scaffold in shaping the future landscape of novel anti-inflammatory drug development.

Drug Discovery and the Role of Artificial Intelligence in Human Healthcare

Abstract: The integration of artificial intelligence (AI) into healthcare and drug development has catalyzed an era of change in medical research and patient care. This research paper examines the evolving landscape of drug development and highlights the central role of artificial intelligence in advancing human healthcare. By combining data-driven approaches with advanced machine learning algorithms, AI can transform target identification, molecular screening, lead optimization and clinical trial design. In healthcare, AI applications include medical image analysis, disease diagnosis, personalized medicine, electronic health information management and more, promising better patient care, cost reduction and advances in medical research. However, these innovations come with challenges such as data protection, bias, regulatory barriers and ethical issues. The article highlights the potential of AI in drug development and healthcare and discusses similar AI-based solutions and methods. It also addresses issues related to data protection, interoperability, data quality, regulatory approval, ethical issues and resource constraints. Responsible development and implementation are critical to the safety and effectiveness of healthcare AI applications. Future work includes improving explainability, promoting knowledge sharing and collaboration, creating ethical frameworks, adapting regulatory processes, integrating AI into clinical practice, and addressing global health disparities. Through collaboration and continued research, AI holds the promise of revolutionizing healthcare, leading to better patient outcomes and a brighter future for medicine.

Targeting Abnormal Tau Phosphorylation for Alzheimer's Therapeutics.

Alzheimer's disease (AD) is a widespread neurodegenerative disorder characterized by progressive memory and cognitive decline, posing a formidable public health challenge. This review explores the intricate interplay between two pivotal players in AD pathogenesis: β-amyloid (Aβ) and tau protein. While the amyloid cascade theory has long dominated AD research, recent developments have ignited debates about its centrality. Aβ plaques and tau NFTs are hallmark pathologies in AD. Aducanumab and lecanemab, monoclonal antibodies targeting Aβ, have been approved, albeit amidst controversy, raising questions about the therapeutic efficacy of Aβ-focused interventions. On the other hand, tau, specifically its hyperphosphorylation, disrupts microtubule stability and contributes to neuronal dysfunction. Various post-translational modifications of tau drive its aggregation into NFTs. Emerging treatments targeting tau, such as GSK-3β and CDK5 inhibitors, have shown promise in preclinical and clinical studies. Restoring the equilibrium between protein kinases and phosphatases, notably protein phosphatase-2A (PP2A), is a promising avenue for AD therapy, as tau is primarily regulated by its phosphorylation state. Activation of tau-specific phosphatases offers potential for mitigating tau pathology. The evolving landscape of AD drug development emphasizes tau-centric therapies and reevaluation of the amyloid cascade hypothesis. Additionally, exploring the role of neuroinflammation and its interaction with tau pathology present promising research directions.

B-cell lymphoma-2 family proteins in the crosshairs: Small molecule inhibitors and activators for cancer therapy.

The B-cell lymphoma-2 (BCL-2) family of proteins plays a crucial role in the regulation of apoptosis, offering a dual mechanism for its control. Numerous studies have established a strong association between gene disorders of these proteins and the proliferation of diverse cancer cell types. Consequently, the identification and development of drugs targeting BCL-2 family proteins have emerged as a prominent area in antitumor therapy. Over the last two decades, several small-molecules have been designed to modulate the protein-protein interactions between anti- and proapoptotic BCL-2 proteins, effectively suppressing tumor growth and metastasis in vivo. The primary focus of research has been on developing BCL-2 homology 3 (BH3) mimetics to target antiapoptotic BCL-2 proteins, thereby competitively releasing proapoptotic BCL-2 proteins and restoring the blocked intrinsic apoptotic program. Additionally, for proapoptotic BCL-2 proteins, exogenous small molecules have been explored to activate cell apoptosis by directly interacting with executioner proteins such as BCL-2-associated X protein (BAX) or BCL-2 homologous antagonist/killer protein (BAK). In this comprehensive review, we summarize the inhibitors and activators (sensitizers) of BCL-2 family proteins developed over the past decades, highlighting their discovery, optimization, preclinical and clinical status, and providing an overall landscape of drug development targeting these proteins for therapeutic purposes.

Exploring drug discovery and development strategies in the context of monkeypox: A comprehensive investigation

Monkeypox is a rare but potentially serious viral infection that has re-emerged as a major public health problem due to recent outbreaks and the increased likelihood of zoonotic transmission. This review delves into the dynamic landscape of monkeypox drug discovery and drug development, revealing the multifaceted challenges and remarkable progress in the fight against this evolving infectious disease. This summary provides a comprehensive overview of ongoing efforts to identify effective antivirals, explore the potential for repurposing medicines, and pioneer innovative therapeutic strategies. As the search for effective treatments gains momentum, the abstract highlights the critical role of collaborative research efforts, the integration of cutting-edge technologies, and the need to adopt a multidisciplinary approach to address monkeypox complexity. In addition, the abstract highlights the critical importance of assessing the current status and future prospects of monkeypox drug development. By analysing the many factors shaping the field, including viral pathogenesis, host immune responses, and viral genetic diversity, this review contributes to a comprehensive understanding of the challenges inherent in addressing monkeypox.

Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology.

The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition. This review dives into the burgeoning world of AI and machine learning (ML), showcasing different applications of AI in clinical pharmacology and the impact of successful AI/ML implementation on drug development and/or regulatory decisions. This review also highlights recommendations for areas of opportunity in clinical pharmacology, including data analysis (e.g., handling large data sets, screening to identify important covariates, and optimizing patient population) and efficiencies (e.g., automation, translation, literature curation, and training). Realizing the benefits of AI in drug development and understanding its value will lead to the successful integration of AI tools in our clinical pharmacology and pharmacometrics armamentarium.

In silico identification of colchicine derivatives as novel and potential inhibitors based on molecular docking and dynamic simulations targeting multifactorial drug targets involved in Alzheimer’s disease

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, characterized by a gradual and steady deterioration in cognitive function over time. At least 50 million people worldwide are considered to have AD or another form of dementia. AD is marked by a gradual decline in cognitive abilities, memory deterioration and neurodegenerative transformations within the brain. The intricate and multifaceted nature of polygenic AD presents significant challenges within the landscape of drug development. The pathophysiology of AD unfolds in a non-linear and dynamic pattern, encompassing various systems and giving rise to a multitude of factors and hypotheses that contribute to the disease’s onset. These encompass theories such as the beta-amyloid hypothesis, cholinergic hypothesis, tau hypothesis, oxidative stress and more. In the realm of drug development, polypharmacological drug profiles have emerged as a strategy that can yield combined or synergistic effects, effectively mitigating undesirable side effects and significantly enhancing the therapeutic efficacy of essential medications. With this concept in mind, our in-silico study sought to delve into the binding interactions of a diverse array of colchicine derivative compounds. These derivatives are chosen for their potential anti-inflammatory, antioxidant, anti-neurodegenerative and neuroprotective properties against Alzheimer’s and other neurodegenerative diseases. We investigated compound interactions with AD-related targets, utilizing comprehensive molecular docking and dynamic simulations. COM111X showed impressive docking with acetylcholinesterase, indicating potential as an anti-Alzheimer’s drug. COM112Y displayed strong docking scores with PDE4D and butyrylcholinesterase, suggesting dual inhibition for Alzheimer’s treatment. Further in vitro and in vivo studies are warranted to explore these findings. Communicated by Ramaswamy H. Sarma

Pediatric Drug Development: Reviewing Challenges and Opportunities by Tracking Innovative Therapies.

The paradigm of pediatric drug development has been evolving in a "carrot-and-stick"-based tactic to address population-specific issues. However, the off-label prescription of adult medicines to pediatric patients remains a feature of clinical practice, which may compromise the age-appropriate evaluation of treatments. Therefore, the United States and the European Pediatric Formulation Initiative have recommended applying nanotechnology-based delivery systems to tackle some of these challenges, particularly applying inorganic, polymeric, and lipid-based nanoparticles. Connected with these, advanced therapy medicinal products (ATMPs) have also been highlighted, with optimistic perspectives for the pediatric population. Despite the results achieved using these innovative therapies, a workforce that congregates pediatric patients and/or caregivers, healthcare stakeholders, drug developers, and physicians continues to be of utmost relevance to promote standardized guidelines for pediatric drug development, enabling a fast lab-to-clinical translation. Therefore, taking into consideration the significance of this topic, this work aims to compile the current landscape of pediatric drug development by (1) outlining the historic regulatory panorama, (2) summarizing the challenges in the development of pediatric drug formulation, and (3) delineating the advantages/disadvantages of using innovative approaches, such as nanomedicines and ATMPs in pediatrics. Moreover, some attention will be given to the role of pharmaceutical technologists and developers in conceiving pediatric medicines.

Palliative care integration for patients on phase I cancer clinical trials

Early integration of palliative care (PC) is recommended as standard of care for patients with advanced cancer. However, challenges remain for both medical oncologists and PC specialists in knowing how to best integrate PC within the dynamic landscape of cancer therapeutics and drug development. Over the last several years in oncology, the success of immunotherapy and molecularly targeted agents has led to a greater demand for novel agents and expansion of objectives of early phase clinical trials. This has in turn created more nuance when discussing prognosis and supporting patients with advanced disease and their caregivers. This review article presents the current literature to support the integration of PC within investigational oncology, as a mechanism to address the evolving needs of patients and caregivers, during and beyond participation on phase I cancer clinical trials.

Transforming Drug Development for Neurological Disorders: Proceedings from a Multidisease Area Workshop

Neurological disorders represent some of the most challenging therapeutic areas for successful drug approvals. The escalating global burden of death and disability for such diseases represents a significant worldwide public health challenge, and the rate of failure of new therapies for chronic progressive disorders of the nervous system is higher relative to other non-neurological conditions. However, progress is emerging rapidly in advancing the drug development landscape in both rare and common neurodegenerative diseases. In October 2022, the Critical Path Institute (C-Path) and the US Food and Drug Administration (FDA) organized a Neuroscience Annual Workshop convening representatives from the drug development industry, academia, the patient community, government agencies, and regulatory agencies regarding the future development of tools and therapies for neurological disorders. This workshop focused on five chronic progressive diseases: Alzheimer's disease, Parkinson's disease, Huntington's disease, Duchenne muscular dystrophy, and inherited ataxias. This special conference report reviews the key points discussed during the three-day dynamic workshop, including shared learnings, and recommendations that promise to catalyze future advancement of novel therapies and drug development tools.

Insights into immuno-oncology drug development landscape with focus on bone metastasis.

Bone is among the main sites of metastasis in breast, prostate and other major cancers. Bone metastases remain incurable causing high mortality, severe skeletal-related effects and decreased quality of life. Despite the success of immunotherapies in oncology, no immunotherapies are approved for bone metastasis and no clear benefit has been observed with approved immunotherapies in treatment of bone metastatic disease. Therefore, it is crucial to consider unique features of tumor microenvironment in bone metastasis when developing novel therapies. The vicious cycle of bone metastasis, referring to crosstalk between tumor and bone cells that enables the tumor cells to grow in the bone microenvironment, is a well-established concept. Very recently, a novel osteoimmuno-oncology (OIO) concept was introduced to the scientific community. OIO emphasizes the significance of interactions between tumor, immune and bone cells in promoting tumor growth in bone metastasis, and it can be used to reveal the most promising targets for bone metastasis. In order to provide an insight into the current immuno-oncology drug development landscape, we used 1stOncology database, a cancer drug development resource to identify novel immunotherapies in preclinical or clinical development for breast and prostate cancer bone metastasis. Based on the database search, 24 immunotherapies were identified in preclinical or clinical development that included evaluation of effects on bone metastasis. This review provides an insight to novel immuno-oncology drug development in the context of bone metastasis. Bone metastases can be approached using different modalities, and tumor microenvironment in bone provides many potential targets for bone metastasis. Noting current increasing interest in the field of OIO, more therapeutic opportunities that primarily target bone metastasis are expected in the future.

Psychedelic therapy in the treatment of addiction: the past, present and future.

Psychedelic therapy has witnessed a resurgence in interest in the last decade from the scientific and medical communities with evidence now building for its safety and efficacy in treating a range of psychiatric disorders including addiction. In this review we will chart the research investigating the role of these interventions in individuals with addiction beginning with an overview of the current socioeconomic impact of addiction, treatment options, and outcomes. We will start by examining historical studies from the first psychedelic research era of the mid-late 1900s, followed by an overview of the available real-world evidence gathered from naturalistic, observational, and survey-based studies. We will then cover modern-day clinical trials of psychedelic therapies in addiction from first-in-human to phase II clinical trials. Finally, we will provide an overview of the different translational human neuropsychopharmacology techniques, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), that can be applied to foster a mechanistic understanding of therapeutic mechanisms. A more granular understanding of the treatment effects of psychedelics will facilitate the optimisation of the psychedelic therapy drug development landscape, and ultimately improve patient outcomes.

Pharmaceutical Analysis in Drug Discovery and Drug Development

Pharmaceutical analysis stands as a linchpin within the intricate and multifaceted landscape of drug development. It assumes a pivotal role in upholding the safety, efficacy and quality of medicinal products at each juncture of their odyssey from conceptualization to market integration. Within this discourse, we delve into the paramount role of pharmaceutical analysis within the sphere of drug development. Pharmaceutical analysis plays an instrumental role in the identification and characterization of lead compounds that harbor therapeutic potential. Techniques such as high-throughput screening, mass spectrometry and nuclear magnetic resonance (NMR) serve as guiding lights in the quest for promising candidates. The elucidation of molecular structures through the means of NMR and X-ray crystallography takes center stage, facilitating a profound comprehension of the intricate interplay between drugs and biological targets. In this review we focused on role of pharmaceutical analysis in drug development and drug discovery. Inadequate pharmaceutical analysis and manufacturing practices can lead to the distribution of unsafe and ineffective medicines. Thus, adherence to cGMP is not only a legal obligation but also a moral imperative to protect public health and maintain the integrity of the pharmaceutical industry.

Overcoming Barriers to Drug Development in Children with CKD.

Like all sick children, children with chronic kidney disease (CKD) need access to safe and effective medicines that have been formulated and examined specifically for them. Despite legislation in the United States (US) and the European Union (EU) that either mandates or incentivizes programs for children, conducting trials to advance the treatment of children continues to prove to be a challenge for drug developers. This is also the case for drug development in children with CKD, where trials face challenges in recruitment and completion, and where there remains a substantial time lag between initial approval of a medicinal product for use in adults and completion of studies that result in the addition of pediatric-specific labeling for the same indication. The Kidney Health Initiative commissioned a workgroup of diverse stakeholders (https://khi.asn-online.org/projects/project.aspx?ID=61), including participants from the Food and Drug Administration (FDA) and the European Medicines Agency (EMA), to think carefully through the challenges in drug development for children with CKD, and how to overcome them. This paper provides an overview of the regulatory frameworks in the US and EU which govern pediatric drug development, the current landscape of drug development and approval for children with CKD, the challenges in conduct and execution of these drug trials, and the progress that has been made to facilitate drug development for children with CKD.

Animal Alternatives for Drug Development

Animal Alternatives for Drug Development