Optimizing Drug Development Research Articles

Computational Approaches for Antimicrobial Peptide Delivery.

Peptides constitute alternative molecules for the treatment of infections caused by bacteria, viruses, fungi, and protozoa. However, their therapeutic effectiveness is often limited by enzymatic degradation, chemical and physical instability, and toxicity toward healthy human cells. To improve their pharmacokinetic (PK) and pharmacodynamic (PD) profiles, novel routes of administration are being explored. Among these, nanoparticles have shown promise as potential carriers for peptides, although the design of delivery vehicles remains a slow and painstaking process, heavily reliant on trial and error. Recently, computational approaches have been introduced to accelerate the development of effective drug delivery systems for peptides. Here we present an overview of some of these computational strategies and discuss their potential to optimize drug development and delivery.

Advancements in the treatment of autoimmune diseases: Integrating artificial intelligence for personalized medicine

The incorporation of artificial intelligence (AI) into medical practice has considerably improved the treatment of autoimmune disorders, opening new avenues for personalized therapy. This study examines advances in AI-driven therapeutic options for autoimmune illnesses, including both current and developing treatments. Traditional therapies for autoimmune illnesses, such as immunosuppressive therapy and biologics, attempt to alleviate symptoms but frequently fall short of offering personalized care. Emerging approaches, such as precision medicine and artificial intelligence, are altering the landscape by harnessing massive volumes of patient data to better customize therapies. AI holds the ability to transform autoimmune disease therapy by enhancing diagnosis, discovering biomarkers, optimizing drug development, and personalized treatment procedures. Real-world applications and case studies are examined to demonstrate how machine learning algorithms have improved treatment tactics for rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. While AI has many advantages, like enhanced diagnosis accuracy and personalized therapy, it also has drawbacks, such as data privacy, the requirement for vast datasets, algorithmic bias, and a lack of explain ability. This study emphasizes the advantages of AI, such as improved patient stratification and predictive modelling, while also discussing its drawbacks, such as ethical problems and the possibility of data exploitation. AI presents intriguing prospects for treating autoimmune diseases, but more research and cooperation are required to overcome current difficulties and completely integrate AI into clinical practice.

Development of Predictive Statistical Model for Gaining Valuable Insights in Pharmaceutical Product Recalls.

The rapid progress in artificial intelligence (AI) has revolutionized problem-solving across various domains. The global challenge of pharmaceutical product recalls imposes the development of effective tools to control and reduce shortage of pharmaceutical products and help avoid such recalls. This study employs AI, specifically machine learning (MI), to analyze critical factors influencing formulation, manufacturing, and formulation complexity which could offer promising avenue for optimizing drug development processes. Utilizing FDAZilla and SafeRX tools, an open database model was constructed, and predictive statistical models were developed using Multivariate Analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) Approach. The study focuses on key descriptors such as delivery route, dosage form, dose, BCS classification, solid-state and physicochemical properties, release type, half-life, and manufacturing complexity. Through statistical analysis, a data simplification process identifies critical descriptors, assigning risk numbers and computing a cumulative risk number to assess product complexity and recall likelihood. Partial Least Square Regression and the LASSO approach established quantitative relationships between key descriptors and cumulative risk numbers. Results have identified key descriptors; BCS Class I, dose number, release profile, and drug half-life influencing product recall risk. The LASSO model further confirms these identified descriptors with 71% accuracy. In conclusion, the study presents a holistic AI and machine learning approach for evaluating and forecasting pharmaceutical product recalls, underscoring the importance of descriptors, formulation complexity, and manufacturing processes in mitigating risks associated with product quality.

DTA-GTOmega: Enhancing Drug-Target Binding Affinity Prediction with Graph Transformers Using OmegaFold Protein Structures

Understanding drug-protein interactions is crucial for elucidating drug mechanisms and optimizing drug development. However, existing methods have limitations in representing the three-dimensional structure of targets and capturing the complex relationships between drugs and targets. This study proposes a new method, DTA-GTOmega, for predicting drug-target binding affinity. DTA-GTOmega utilizes OmegaFold to predict protein three-dimensional structure and construct target graphs, while processing drug SMILES sequences with RDKit to generate drug graphs. By employing multi-layer graph transformer modules and co-attention modules, this method effectively integrates atomic-level features of drugs and residue-level features of targets, accurately modeling the complex interactions between drugs and targets, thereby significantly improving the accuracy of binding affinity predictions. Our method outperforms existing techniques on benchmark datasets such as KIBA, Davis, and BindingDB_Kd under cold-start setting. Moreover, DTA-GTOmega demonstrates competitive performance in real-world DTI scenarios involving DrugBank data and drug-target interactions related to cardiovascular and nervous system-related diseases, highlighting its robust generalization capabilities. Additionally, the introduced DTI evaluation metrics further validate DTA-GTOmega’s potential in handling imbalanced data.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Subcellular Drug Distribution: Exploring Organelle-Specific Characteristics for Enhanced Therapeutic Efficacy.

The efficacy and potential toxicity of drug treatments depends on the drug concentration at its site of action, intricately linked to its distribution within diverse organelles of mammalian cells. These organelles, including the nucleus, endosome, lysosome, mitochondria, endoplasmic reticulum, Golgi apparatus, lipid droplets, exosomes, and membrane-less structures, create distinct sub-compartments within the cell, each with unique biological features. Certain structures within these sub-compartments possess the ability to selectively accumulate or exclude drugs based on their physicochemical attributes, directly impacting drug efficacy. Under pathological conditions, such as cancer, many cells undergo dynamic alterations in subcellular organelles, leading to changes in the active concentration of drugs. A mechanistic and quantitative understanding of how organelle characteristics and abundance alter drug partition coefficients is crucial. This review explores biological factors and physicochemical properties influencing subcellular drug distribution, alongside strategies for modulation to enhance efficacy. Additionally, we discuss physiologically based computational models for subcellular drug distribution, providing a quantifiable means to simulate and predict drug distribution at the subcellular level, with the potential to optimize drug development strategies.

Pathology and pathobiology of pulmonary hypertension: current insights and future directions.

In recent years, major advances have been made in the understanding of the cellular and molecular mechanisms driving pulmonary vascular remodelling in various forms of pulmonary hypertension, including pulmonary arterial hypertension, pulmonary hypertension associated with left heart disease, pulmonary hypertension associated with chronic lung disease and hypoxia, and chronic thromboembolic pulmonary hypertension. However, the survival rates for these different forms of pulmonary hypertension remain unsatisfactory, underscoring the crucial need to more effectively translate innovative scientific knowledge into healthcare interventions. In these proceedings of the 7th World Symposium on Pulmonary Hypertension, we delve into recent developments in the field of pathology and pathophysiology, prioritising them while questioning their relevance to different subsets of pulmonary hypertension. In addition, we explore how the latest omics and other technological advances can help us better and more rapidly understand the myriad basic mechanisms contributing to the initiation and progression of pulmonary vascular remodelling. Finally, we discuss strategies aimed at improving patient care, optimising drug development, and providing essential support to advance research in this field.

Getting the Dose Right in Drug Development for Rare Diseases: Barriers and Enablers.

In the relentless pursuit of optimizing drug development, the intricate process of determining the ideal dosage unfolds. This involves "dose-finding" studies, crucial for providing insights into subsequent registration trials. However, the challenges intensify when tackling rare diseases. The complexity arises from poorly understood pathophysiologies, scarcity of appropriate animal models, and limited natural history understanding. The inherent heterogeneity, coupled with challenges in defining clinical end points, poses substantial challenges, hindering the utility of available data. The small affected population, low disease awareness, and restricted healthcare access compound the difficulty in conducting dose-finding studies. This white paper delves into critical dose selection aspects, focusing on key therapeutic areas, such as oncology, neurology, hepatology, metabolic rare diseases. It also explores dose selection challenges posed by pediatric rare diseases as well as novel modalities, including enzyme replacement therapies, cell and gene therapies, and oligonucleotides. Several examples emphasize the pivotal role of clinical pharmacology in navigating the complexities associated with these diseases and emerging treatment modalities.

Optimizing Drug Development: Harnessing the Sustainability of Pharmaceutical Cocrystals.

Environmental impacts of the industrial revolution necessitate adoption of sustainable practices in all areas of development. The pharmaceutical industry faces increasing pressure to minimize its ecological footprint due to its significant contribution to environmental pollution. Over the past two decades, pharmaceutical cocrystals have received immense popularity due to their ability to optimize the critical attributes of active pharmaceutical ingredients and presented an avenue to bring improved drug products to the market. This review explores the potential of pharmaceutical cocrystals as an ecofriendly alternative to traditional solid forms, offering a sustainable approach to drug development. From reducing the number of required doses to improving the stability of actives, from eliminating synthetic operations to using pharmaceutically approved chemicals, from the use of continuous and solvent-free manufacturing methods to leveraging published data on the safety and toxicology, the cocrystallization approach contributes to sustainability of drug development. The latest trends suggest a promising role of pharmaceutical cocrystals in bringing novel and improved medicines to the market, which has been further fuelled by the recent guidance from the major regulatory agencies.

Interplay of OATP1A/1B/2B1 uptake transporters and ABCB1 and ABCG2 efflux transporters in the handling of bilirubin and drugs

Transmembrane drug transporters can be important determinants of the pharmacokinetics, efficacy, and safety profiles of drugs. To investigate the potential cooperative and/or counteracting interplay of OATP1A/1B/2B1 uptake transporters and ABCB1 and ABCG2 efflux transporters in physiology and pharmacology, we generated a new mouse model (Bab12), deficient for Slco1a/1b, Slco2b1, Abcb1a/1b and Abcg2. Bab12 mice were viable and fertile. We compared wild-type, Slco1a/1b/2b1-/-, Abcb1a/1b;Abcg2-/- and Bab12 strains. Endogenous plasma conjugated bilirubin levels ranked as follows: wild-type = Abcb1a/1b;Abcg2-/- <<Slco1a/1b/2b1-/- <Bab12 mice. Plasma levels of rosuvastatin and fexofenadine were elevated in Slco1a/1b/2b1-/- and Abcb1a/1b;Abcg2-/- mice compared to wild-type, and dramatically increased in Bab12 mice. Although systemic exposure of larotrectinib and repotrectinib was substantially increased in the separate multidrug transporter knockout strains, no additive effects were observed in the combination Bab12 mice. Significantly higher plasma exposure of fluvastatin and pravastatin was only found in Slco1a/1b/2b1-deficient mice. However, noticeable transport by Slco1a/1b/2b1 and Abcb1a/1b and Abcg2 across the BBB was observed for fluvastatin and pravastatin, respectively, by comparing Bab12 mice with Abcb1a/1b;Abcg2-/- or Slco1a/1b/2b1-/- mice. Quite varying behavior in plasma exposure of erlotinib and its metabolites was observed among these strains. Bab12 mice revealed that Abcb1a/1b and/or Abcg2 can contribute to conjugated bilirubin elimination when Slco1a/1b/2b1 are absent. Our results suggest that the interplay of Slco1a/1b/2b1, Abcb1a/1b, and Abcg2 could markedly affect the pharmacokinetics of some, but not all drugs and metabolites. The Bab12 mouse model will represent a useful tool for optimizing drug development and clinical application, including efficacy and safety.

Individualized Pharmacotherapy Utilizing Genetic Biomarkers and Novel In Vitro Systems As Predictive Tools for Optimal Drug Development and Treatment.

In the area of drug development and clinical pharmacotherapy, a profound understanding of the pharmacokinetics and potential adverse reactions associated with the drug under investigation is paramount. Essential to this endeavor is a comprehensive understanding about interindividual variations in absorption, distribution, metabolism, and excretion (ADME) genetics and the predictive capabilities of in vitro systems, shedding light on metabolite formation and the risk of adverse drug reactions (ADRs). Both the domains of pharmacogenomics and the advancement of in vitro systems are experiencing rapid expansion. Here we present an update on these burgeoning fields, providing an overview of their current status and illuminating potential future directions. SIGNIFICANCE STATEMENT: There is very rapid development in the area of pharmacogenomics and in vitro systems for predicting drug pharmacokinetics and risk for adverse drug reactions. We provide an update of the current status of pharmacogenomics and developed in vitro systems on these aspects aimed to achieve a better personalized pharmacotherapy.

HIPK2 C-terminal domain inhibits NF-κB signaling and renal inflammation in kidney injury.

HIPK2 is a multifunctional kinase that acts as a key pathogenic mediator of chronic kidney disease and fibrosis. It acts as a central effector of multiple signaling pathways implicated in kidney injury, such as TGF-β/Smad3-mediated extracellular matrix accumulation, NF-κB-mediated inflammation, and p53-mediated apoptosis. Thus, a better understanding of the specific HIPK2 regions necessary for distinct downstream pathway activation is critical for optimal drug development for CKD. Our study now shows that caspase-6-mediated removal of the C-terminal region of HIPK2 (HIPK2-CT) lead to hyperactive p65 NF-κB transcriptional response in kidney cells. In contrast, the expression of cleaved HIPK2-CT fragment could restrain the NF-κB transcriptional activity by cytoplasmic sequestration of p65 and the attenuation of IκBα degradation. Therefore, we examined whether HIPK2-CT expression can be exploited to restrain renal inflammation in vivo. The induction of HIPK2-CT overexpression in kidney tubular cells attenuated p65 nuclear translocation, expression of inflammatory cytokines, and macrophage infiltration in the kidneys of mice with unilateral ureteral obstruction and LPS-induced acute kidney injury. Collectively, our findings indicate that the HIPK2-CT is involved in the regulation of nuclear NF-κB transcriptional activity and that HIPK2-CT or its analogs could be further exploited as potential antiinflammatory agents to treat kidney disease.

Assessment of Dosing Strategies for Pediatric Drug Products.

Pediatric drug dosing is challenged by the heterogeneity of developing physiology and ethical considerations surrounding a vulnerable population. Often, pediatric drug dosing leverages findings from the adult population; however, recent regulatory efforts have motivated drug sponsors to pursue pediatric-specific programs to meet an unmet medical need and improve pediatric drug labeling. This paradigm is further complicated by the pathophysiological implications of obesity on drug distribution and metabolism and the roles that body composition and body size play in drug dosing. Therefore, we sought to understand the landscape of pediatric drug dosing by characterizing the dosing strategies from drug products recently approved for pediatric indications identified using FDA Drug Databases and analyze the impact of body size descriptors (age, body surface area, weight) on drug pharmacokinetics for several selected antipsychotics approved in pediatric patients. Our review of these pediatric databases revealed a dependence on body size-guided dosing, with 68% of dosing in pediatric drug labelings being dependent on knowing either the age, body surface area, or weight of the patient to guide dosing for pediatric patients. This dependence on body size-guided dosing drives the need for special consideration when dosing a drug in overweight and obese patients. Exploratory pharmacokinetic analyses in antipsychotics illustrate possible effects of drug exposure when applying different dosing strategies for this class of drugs. Future efforts should aim to further understand the pediatric drug dosing and obesity paradigm across pediatric age ranges and drug classes to optimize drug development and clinical care for this patient population.

How to develop new systemic treatments in ultra-rare cancers with high unmet needs? The case of alveolar soft-part sarcoma

Developing new drugs or generating evidence for existing drugs in new indications for ultra-rare cancers is complex and carries a high-risk of failure. This gets even harder in ultra-rare tumours, which have an annual incidence of 1 per 1,000,000 population or less. Here, we illustrate the problem of adequate evidence generation in ultra-rare tumours, using Alveolar Soft-Part Sarcomas (ASPS) – an ultra-rare sarcoma newly diagnosed in approximately 60 persons a year in the European Union – as an exemplar case showing challenges in development despite being potentially relevant for classes of agents. We discuss some possible approaches for addressing such challenges, especially focussing on constructive collaboration between academic groups, patients and advocates, drug manufacturers, and regulators to optimise drug development in ultra-rare cancers. This article, written by various European stakeholders, proposes a way forward to ultimately get better options for patients with ultra-rare cancers.

THE IMPACT OF DIGITIZATION ON PHARMACEUTICAL RESEARCH AND DEVELOPMENT MANAGEMENT

This article first introduces the background, concepts, and theories of drug development management and digitization. It then discusses the issues in drug development management and analyzes the impact of digitization on drug development. Combining theories related to drug development management and digitization, it explores the influence of introducing digital technology and data products on optimizing drug development management.

Challenges And Solutions in Rare Disease Treatment: Value-Based Healthcare

This study is based on the realities of challenges within the field of rare disease treatment, as well as the concept of value-based healthcare. By analyzing treatment plans, rehabilitative care, cost-sharing, prospects for drug development markets and investment returns, as well as policy support, the paper delves into the complexities and diversities of rare disease treatment. Within the research, the focus is on the challenges of rare disease treatment, leveraging the principles of value-based healthcare. It discusses how personalized treatment plans, interdisciplinary collaboration, and reasonable cost-sharing can enhance patient treatment outcomes and quality of life. The article employs methods such as literature reviews and case analyses to deeply explore and analyze rare disease treatment from various angles. The research findings indicate that personalized treatment plans and interdisciplinary collaboration can significantly enhance treatment outcomes and quality of life for rare disease patients. Reasonable cost-sharing models and trends in drug development markets present opportunities for the sustainable growth of the rare disease field. Additionally, policy support and international cooperation mechanisms play vital roles in advancing rare disease treatment. Based on these research findings, the article proposes future research prospects, including strengthening data collection and analysis, deepening international collaboration, and optimizing drug development strategies.

Quality by Design in Pharmaceuticals: A Review of its Impact on Regulatory Compliance and Product Quality.

The pharmaceutical industry has embraced the quality-by-design (QbD) approach as a promising development, formulation and manufacturing strategy. QbD provides a systematic and science-based framework for designing and producing high-quality products, with a particular focus on identifying, assessing and controlling risks throughout the development process. This review aims to assess the benefits of implementing QbD in pharmaceutical processes, evaluate its impact on regulatory compliance and explore its potential to enhance drug product quality. The primary objective of this review is to evaluate the influence of QbD on pharmaceutical development and manufacturing processes. It also seeks to examine the regulatory requirements associated with the implementation of QbD and highlight the advantages of this approach in terms of product quality and cost-effectiveness. Additionally, the review aims to explore the potential of QbD in improving the safety and efficacy of drug products. The QbD approach holds tremendous potential to revolutionize the pharmaceutical industry by optimizing drug development & manufacturing processes, reducing costs and enhancing product quality and consistency. However, implementing QbD requires a comprehensive understanding of the underlying science, as well as strict adherence to regulatory requirements in drug development and manufacturing. In conclusion, by embracing the QbD approach, the pharmaceutical industry can ensure the production of safe, effective and regulation-compliant products while simultaneously improving process efficiency. This strategic shift toward QbD represents a pivotal step in advancing pharmaceutical research and manufacturing capabilities, ultimately benefiting both the industry and more importantly, patients worldwide.

PP132 Health Technology Assessment Agencies’ Expectations Regarding Patient Experience Data in Australia, China, And Japan

IntroductionHealth technology assessment (HTA) agencies are increasingly embracing patient experience data (PED) to support reimbursement decisions. This study aimed to review HTA agencies’ expectations with regards to utilizing PED to support drug reimbursement in Australia, Japan, and China.MethodsPublished HTA guidance documents were reviewed in 2021 to identify any PED-specific information. If available, recommendations related to the type of PED (e.g., generic vs. disease-specific clinical outcomes assessment (COA)); COA validation, analyses, endpoints and interpretation; and the interest in PED beyond COA in HTA decision-making (e.g., patient preference information) were reviewed. Literature review and semi-structured interviews with key opinion leaders (KOLs) were conducted to further explore these themes and future trends with regards to PED.ResultsAustralia’s Pharmaceutical Benefits Advisory Committee guidance document includes a dedicated section on patient-reported outcomes (PRO), providing details on preferred PRO instruments; validation expectations; and recommended methods to explore score interpretation, assess and report PRO results and handle missing data. While PED derived from non-PRO sources are not discussed in the guidance, the KOL noted that they should not be ruled out. Japan’s Center for Outcomes Research and Economic Evaluation for Health guideline includes a section dedicated to PROs without details related to instrument validation, analyses and interpretation, however, is focused on the use of PRO to inform health economic assessments. In China, the HTA center of China National Health Commission drafted two disease-specific technical guidance documents recommending the inclusion of PROs in efficacy assessments and use of instruments relevant in the Chinese population; these points were echoed by the KOL interviewed.ConclusionsThere are recommendations on PED use included in country-specific guidance documents, however their level of detail varies greatly. Knowing each agency’s expectations with regards to PED is key when submitting HTA evidence dossiers and should be considered early in clinical trial design to integrate market access perspectives and optimize drug development.

PP131 Health Technology Assessment Agencies’ Expectations Regarding Patient Experience Data in Europe

IntroductionHealth technology assessment (HTA) agencies are increasingly embracing patient experience data (PED) to support reimbursement decisions. This study aimed to describe the European Network for HTA (EUnetHTA) and HTA agencies expectations regarding PED to support reimbursement in France, Germany, Italy, Spain and the UK.MethodsPublished HTA guidance documents were reviewed to identify recommendations related to clinical outcomes assessment (COA) (including disease-specificity, validation, analyses, endpoints and interpretation) and other forms of PED (e.g., patient preference information) in HTA decision-making. Insights from guidance documents were supplemented with a review of literature and published HTA cases and interviews with key opinion leaders (KOLs) focused on current and future states.ResultsThe German and French guidance documents include PED recommendations focused on relevant COA and health-related quality of life data, without detailing preferred COA measures. However, key differences were noted between these two countries in the methodological approaches regarding responder definitions, acceptable missing data threshold and multiplicity analyses. These differences were reinforced by the case studies and the KOLs. UK’s sources also focused on COA, in general proposing specific use of the EQ-5D to derive utility values for modelling, but included limited details on other PED-related elements. The Italian and Spanish guidance documents do not detail COA or other PED expectations, but the Italian KOL described that COA is considered if submitted. The currently developed EUnetHTA21 guidelines include PED-related information that bear the signature of certain individual HTA bodies. Globally, there is limited interest in PED beyond COA across the agencies.ConclusionsThe level of expectations with regards to PED varies across EUnetHTA and several European HTA agencies. Interest in PED derived from non-COA sources is limited across the countries. Knowing each agency’s expectations with regards to PED is key when submitting HTA evidence dossiers and should be considered early in clinical trial design to integrate market access perspectives and optimize drug development. Global harmonization would help advancing PED measurement standards.

Easing the data sharing burden: perspectives and principles to successfully leverage AD data repositories, infrastructures and functionality

AbstractCritical Path Institute (C‐Path) is a nonprofit organization that brings together patient groups, academic institutions, the pharmaceutical industry and regulatory agencies from around the world to improve public health. C‐Path provides the legal, scientific, and regulatory infrastructure to generate a unique neutral environment for medical product development, supported by more than a decade of experience in data standards development, platform development and hosting, patient‐level data privacy stewardship, data platform security, and controlled access methodology. The Critical Path for Alzheimer’s Disease (CPAD) is a pre‐competitive consortium that aims to promote, support, and manage a diverse source and format of data and facilitate knowledge sharing from the Alzheimer disease (AD) drug development community. The CPAD database currently contains 65 clinical trials and observational studies, with over 93,000 individual anonymized patient‐level records, with global access to many datasets through the C‐Path Online Data Repository (CODR). The unparalleled breadth, depth and wealth of data will enable us to discover yet‐to‐be identified unmet needs and unanswered questions. Further, CODR is interoperable with the Global Alzheimer’s Association Interactive Network (GAAIN), Vivli and other data platforms. The secure and neutral data sharing framework of CPAD has resulted in the development of several regulatory‐grade tools that help optimize drug development and accelerate bringing new therapies to patients in need. Sufficient case examples of successful data sharing are now available and should inform and encourage future data sharing initiatives.