The Promise of Therapeutic Psilocybin: An Evaluation of the 134 Clinical Trials, 54 Potential Indications, and 0 Marketing Approvals on ClinicalTrials.gov.

Introduction: Psilocybin, a tryptamine psychedelic, has been touted in the media both historically and recently as a potential game-changing mental health therapeutic. ClinicalTrials.gov has over one hundred and thirty psilocybin clinical trials listed covering the last twenty years. The single most important aspect of any therapeutic is to gain approval for marketing and thus enter the real-world phase of development. A typical new chemical entity progresses from inception to US Food and Drug Administration (FDA) approval in approximately 12 years and seeks approval for a single indication. Methods: An observational study was conducted with the available information on the ClinicalTrials.gov site to observe the extent of progress made demonstrating the clinical utility of psilocybin. Results: The results showed 134 psilocybin trials typically unblinded studies of 10-20 participants, recruited over years at a single site. Additionally, there have been only three advanced trials (1 Phase 2/3 and 2 Phase 3) submitted, and only in the last two years. Discussion: The hundreds of psilocybin clinical trials initiated over the past twenty years comprising a myriad of potential indications may actually be slowing this potential game-changing mental health therapeutic agent's approval and is costing excessive amounts of capital. To fully evaluate the actual potential of psilocybin, purposeful clinical trials need to be designed well, executed efficiently, and analyzed utilizing sequential and statistically valid processes for each potential indication. This will require a change from the current exploratory forays to defined, well-funded, sequential pharmaceutical development practices, including adequate and appropriate blinding of studies, statistical design to determine the number of participants and more importantly, professional expertise in conducting multicenter trials. Unfortunately, these results demonstrate little real progress towards FDA approval of psilocybin and a field with no clear direction forward.

Enhancing drug development and clinical studies with patient-centric sampling using microsampling techniques: Opportunities, challenges, and insights into liquid chromatography-mass spectrometry strategies.

Microsampling has revolutionized pharmaceutical drug development and clinical research by reducing sample volume requirements, allowing sample collection at home or nontraditional sites, minimizing animal and patient burden, and enabling more flexible study designs. This perspective paper discusses the transformative impact of microsampling and patient-centric sampling (PCS) techniques, emphasizing their advantages in drug development and clinical trials. We highlight the integration of liquid chromatography-mass spectrometry (LC-MS) strategies for analyzing PCS samples, focusing on our research experience and a review of current literatures. The paper reviews commercially available PCS devices, their regulatory status, and their application in clinical trials, underscoring the benefits of PCS in expanding patient enrollment diversity and improving study designs. We also address the operational challenges of implementing PCS, including the need for bridging studies to ensure data comparability between traditional and microsampling methods, and the analytical challenges posed by PCS samples. The paper proposes future directions for PCS, including the development of global regulatory standards, technological advancements to enhance user experience, the increased concern of sustainability and patient data privacy, and the integration of PCS with other technologies for improved performance in drug development and clinical studies. By advancing microsampling and PCS techniques, we aim to foster patient-centric approaches in pharmaceutical sciences, ultimately enhancing patient care and treatment efficacy.

Stability-based approaches in chemoproteomics.

Target deconvolution can help understand how compounds exert therapeutic effects and can accelerate drug discovery by helping optimise safety and efficacy, revealing mechanisms of action, anticipate off-target effects and identifying opportunities for therapeutic expansion. Chemoproteomics, a combination of chemical biology with mass spectrometry has transformed target deconvolution. This review discusses modification-free chemoproteomic approaches that leverage the change in protein thermodynamics induced by small molecule ligand binding. Unlike modification-based methods relying on enriching specific protein targets, these approaches offer proteome-wide evaluations, driven by advancements in mass spectrometry sensitivity, increasing proteome coverage and quantitation methods. Advances in methods based on denaturation/precipitation by thermal or chemical denaturation, or by protease degradation are evaluated, emphasising the evolving landscape of chemoproteomics and its potential impact on future drug-development strategies.

Accelerating Drug Development Using Spatial Multi-omics.

SUMMARY: Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility of spatial biology data in accelerating drug programs, however, requires balancing exploratory discovery investigations against scalable and clinically applicable spatial biomarker analysis.

Leveraging Modeling and Simulation to Enhance the Efficiency of Bioequivalence Approaches for Generic Drugs: Highlights from the 2023 Generic Drug Science and Research Initiatives Public Workshop.

The 2023 Generic Drug Science and Research Initiative Public Workshop organized by the U.S. Food and Drug Administration (FDA) discussed the research needs to improve and enhance bioequivalence (BE) approaches for generic drug development. FDA takes such research needs and panel discussions into account to develop its Generic Drug User Fee Amendments III (GDUFA III) Science and Research Initiatives specific to generics. During the five workshop sessions, presentations and panel discussions focused on identifying and addressing scientific gaps and research needs related to nitrosamine impurity issues, BE assessment for oral products, innovative BE approaches for long-acting injectable products, alternative BE approaches for orally inhaled products, and advanced BE methods for topical products. Specifically, this report highlights the discussions on how to improve BE assessment for developing generic drug products based on research priorities for leveraging quantitative methods and modeling, as well as artificial intelligence/machine learning (AI/ML).

Drug development and potential targets for Cushing's syndrome.

Cushing's syndrome (CS) is a complex disorder characterized by the excessive secretion of cortisol, with Cushing's disease (CD), particularly associated with pituitary tumors, exhibiting heightened morbidity and mortality. Although transsphenoidal pituitary surgery (TSS) stands as the primary treatment for CD, there is a crucial need to optimize patient prognosis. Current medical therapy serves as an adjunctive measure due to its unsatisfactory efficacy and unpredictable side effects. In this comprehensive review, we delve into recent advances in understanding the pathogenesis of CS and explore therapeutic options by conducting a critical analysis of potential drug targets and candidates. Additionally, we provide an overview of the design strategy employed in previously reported candidates, along with a summary of structure-activity relationship (SAR) analyses and their biological efficacy. This review aims to contribute valuable insights to the evolving landscape of CS research, shedding light on potential avenues for therapeutic development.

Identification of an inhibitory pocket in falcilysin provides a new avenue for malaria drug development.

Identification of new druggable protein targets remains the key challenge in the current antimalarial development efforts. Here we used mass-spectrometry-based cellular thermal shift assay (MS-CETSA) to identify potential targets of several antimalarials and drug candidates. We found that falcilysin (FLN) is a common binding partner for several drug candidates such as MK-4815, MMV000848, and MMV665806 but also interacts with quinoline drugs such as chloroquine and mefloquine. Enzymatic assays showed that these compounds can inhibit FLN proteolytic activity. Their interaction with FLN was explored systematically by isothermal titration calorimetry and X-ray crystallography, revealing a shared hydrophobic pocket in the catalytic chamber of the enzyme. Characterization of transgenic cell lines with lowered FLN expression demonstrated statistically significant increases in susceptibility toward MK-4815, MMV000848, and several quinolines. Importantly, the hydrophobic pocket of FLN appears amenable to inhibition and the structures reported here can guide the development of novel drugs against malaria.

Implementation of a Three-Way Comparability Assessment for a Bioanalytical Anti-Drug Antibody Method.

Immunogenicity evaluation is a critical part of drug development. Regulatory guidelines from multiple health agencies provide recommendations for the development and validation of anti-drug antibody (ADA) assays to assess immunogenicity in clinical trials. These recommendations primarily describe an ADA method run in one bioanalytical laboratory supporting a biotherapeutic molecule; however, there are increasing instances that may necessitate the support of the ADA method being run in more than one laboratory. A program can rapidly expand into multiple clinical studies within one or multiple countries, where the most appropriate way to support the program is by having multiple laboratories perform ADA sample analysis. In addition, there may be certain country-specific challenges that may make it infeasible to transport samples outside of the country for analysis. China for example has a lengthy sample exportation process that has potential to negatively impact study timelines. If multiple laboratories analyze samples using the same ADA method, comparable method performance should be established. Here, we describe a three-way assessment of ADA assay comparability between two US-based bioanalytical laboratories and one based in China.

Discovery of a novel and highly selective JAK3 inhibitor as a potent hair growth promoter.

JAK-STAT signalling pathway inhibitors have emerged as promising therapeutic agents for the treatment of hair loss. Among different JAK isoforms, JAK3 has become an ideal target for drug discovery because it only regulates a narrow spectrum of γc cytokines. Here, we report the discovery of MJ04, a novel and highly selective 3-pyrimidinylazaindole based JAK3 inhibitor, as a potential hair growth promoter with an IC50 of 2.03 nM. During in vivo efficacy assays, topical application of MJ04 on DHT-challenged AGA and athymic nude mice resulted in early onset of hair regrowth. Furthermore, MJ04 significantly promoted the growth of human hair follicles under ex-vivo conditions. MJ04 exhibited a reasonably good pharmacokinetic profile and demonstrated a favourable safety profile under in vivo and in vitro conditions. Taken together, we report MJ04 as a highly potent and selective JAK3 inhibitor that exhibits overall properties suitable for topical drug development and advancement to human clinical trials.

Identification and Biological Evaluation of a Novel Small-Molecule Inhibitor of Ricin Toxin.

The plant-derived toxin ricin is classified as a type 2 ribosome-inactivating protein (RIP) and currently lacks effective clinical antidotes. The toxicity of ricin is mainly due to its ricin toxin A chain (RTA), which has become an important target for drug development. Previous studies have identified two essential binding pockets in the active site of RTA, but most existing inhibitors only target one of these pockets. In this study, we used computer-aided virtual screening to identify a compound called RSMI-29, which potentially interacts with both active pockets of RTA. We found that RSMI-29 can directly bind to RTA and effectively attenuate protein synthesis inhibition and rRNA depurination induced by RTA or ricin, thereby inhibiting their cytotoxic effects on cells in vitro. Moreover, RSMI-29 significantly reduced ricin-mediated damage to the liver, spleen, intestine, and lungs in mice, demonstrating its detoxification effect against ricin in vivo. RSMI-29 also exhibited excellent drug-like properties, featuring a typical structural moiety of known sulfonamides and barbiturates. These findings suggest that RSMI-29 is a novel small-molecule inhibitor that specifically targets ricin toxin A chain, providing a potential therapeutic option for ricin intoxication.

Targeting Moonlighting Enzymes in Cancer.

Moonlighting enzymes are multifunctional proteins that perform multiple functions beyond their primary role as catalytic enzymes. Extensive research and clinical practice have demonstrated their pivotal roles in the development and progression of cancer, making them promising targets for drug development. This article delves into multiple notable moonlighting enzymes, including GSK-3, GAPDH, and ENO1, and with a particular emphasis on an enigmatic phosphatase, PTP4A3. We scrutinize their distinct roles in cancer and the mechanisms that dictate their ability to switch roles. Lastly, we discuss the potential of an innovative approach to develop drugs targeting these moonlighting enzymes: target protein degradation. This strategy holds promise for effectively tackling moonlighting enzymes in the context of cancer therapy.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.

Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction.

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making.

INTRODUCTION: While pharmaceutical companies aim to leverage real-world data (RWD) to bridge the gap between clinical drug development and real-world patient outcomes, extant research has mainly focused on the use of social media in a post-approval safety-surveillance setting. Recent regulatory and technological developments indicate that social media may serve as a rich source to expand the evidence base to pre-approval and drug development activities. However, use cases related to drug development have been largely omitted, thereby missing some of the benefits of RWD. In addition, an applied end-to-end understanding of RWD rooted in both industry and regulations is lacking. OBJECTIVE: We aimed to investigate how social media can be used as a source of RWD to support regulatory decision making and drug development in the pharmaceutical industry. We aimed to specifically explore the data pipeline and examine how social-media derived RWD can align with regulatory guidance from the US Food and Drug Administration and industry needs. METHODS: A machine learning pipeline was developed to extract patient insights related to anticoagulants from X (Twitter) data. These findings were then analysed from an industry perspective, and complemented by interviews with professionals from a pharmaceutical company. RESULTS: The analysis reveals several use cases where RWD derived from social media can be beneficial, particularly in generating hypotheses around patient and therapeutic area needs. We also note certain limitations of social media data, particularly around inferring causality. CONCLUSIONS: Social media display considerable potential as a source of RWD for guiding efforts in pharmaceutical drug development and pre-approval settings. Although further regulatory guidance on the use of social media for RWD is needed to encourage its use, regulatory and technological developments are suggested to warrant at least exploratory uses for drug development.

Assessing Environmental Risks during the Drug Development Process for Parasitic Vector-Borne Diseases: A Critical Reflection.

Parasitic vector-borne diseases (VBDs) represent nearly 20% of the global burden of infectious diseases. Moreover, the spread of VBDs is enhanced by global travel, urbanization, and climate change. Treatment of VBDs faces challenges due to limitations of existing drugs, as the potential for side effects in nontarget species raises significant environmental concerns. Consequently, considering environmental risks early in drug development processes is critically important. Here, we examine the environmental risk assessment process for veterinary medicinal products in the European Union and identify major gaps in the ecotoxicity data of these drugs. By highlighting the scarcity of ecotoxicological data for commonly used antiparasitic drugs, we stress the urgent need for considering the One Health concept. We advocate for employing predictive tools and nonanimal methodologies such as New Approach Methodologies at early stages of antiparasitic drug research and development. Furthermore, adopting progressive approaches to mitigate ecological risks requires the integration of nonstandard tests that account for real-world complexities and use environmentally relevant exposure scenarios. Such a strategy is vital for a sustainable drug development process as it adheres to the principles of One Health, ultimately contributing to a healthier and more sustainable world.

Pediatric Cancer Drug Development: Leveraging Insights in Cancer Biology and the Evolving Regulatory Landscape to Address Challenges and Guide Further Progress.

The discovery and development of anticancer drugs for pediatric patients have historically languished when compared to both past and recent activity in drug development for adult patients, notably the dramatic spike of targeted and immune-oncology therapies. The reasons for this difference are multifactorial. Recent changes in the regulatory landscape surrounding pediatric cancer drug development and the understanding that some pediatric cancers are driven by genetic perturbations that also drive disparate adult cancers afford new opportunities. The unique cancer-initiating events and dependencies of many pediatric cancers, however, require additional pediatric-specific strategies. Research efforts to unravel the underlying biology of pediatric cancers, innovative clinical trial designs, model-informed drug development, extrapolation from adult data, addressing the unique considerations in pediatric patients, and use of pediatric appropriate formulations, should all be considered for efficient development and dosage optimization of anticancer drugs for pediatric patients.

Challenges and opportunities in oncology drug development and clinical research in China.

As one of the world's most populous countries, China bears a heavy burden and a broad spectrum of cancers, including unique types, providing a unique environment for drug research and development. In recent years, China has leapt forward in oncology drug development and clinical trials, presenting new opportunities and challenges.

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.