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Although public–private collaboration abounds in addressing societal grand challenges, little is known about its performance relative to private-sector collaboration. Drawing from the innovation collaboration literature, and qualitative insights from interviews and contracts, we argue that collaboration risk impairs task performance of public–private versus private–private collaboration. We attribute this to two collaboration risk mechanisms: cooperation problems arising from misaligned social versus economic incentives, and coordination problems due to differences in organizational governance and processes. We test our hypotheses using the development history of 2,496 antimicrobial drugs from 1995–2019. After accounting for endogenous selection into collaboration, we find that public–private collaboration is less effective than private–private collaboration. However, this performance gap reduces for innovation tasks under high technological uncertainty (i.e., in the discovery stage and for developing new drug action mechanisms). To probe the distinctive cooperation and coordination mechanisms, we also examine innovation task efficiency. Findings reveal a wider gap in task effectiveness but not efficiency between public–private and private–private collaboration, suggesting more significant cooperation than coordination problems. Our theoretical and practical insights pertain to whether, when, and how public–private and private–private collaboration offer viable pathways for socially beneficial innovation tasks. [ABSTRACT FROM AUTHOR]

Strategic collaborations are critical to the success of efforts aimed at discovering new drugs and therapies in the biopharmaceutical industry. These collaborations aim to leverage domain expertise, asymmetries in production costs and/or capabilities to improve efficiency. Despite increasing collaborations, the biopharmaceutical industry lacks a structured guideline for choosing contracts. We present contract models with effort-based formulations that capture the key characteristics of biopharmaceutical operations and analyse incentive mechanisms such as fixed payment, revenue-sharing, risk-sharing, and cost-sharing in biopharmaceutical collaborations. We show that traditional incentive schemes do not achieve supply chain coordination, although they are commonly used in the industry. We introduce a new contract model called fee-for-effort-and-output contract that encourages the parties to exert higher efforts by offering discounts on their operating costs, and show that this contract achieves coordination with an appropriate selection of contract parameters. We also investigate the efficiency of noncoordinating contracts with traditional incentive schemes and identify the capability and cost structures under which they achieve the highest efficiency possible, to both determine the next-best alternatives and explain their popularity in practice. [ABSTRACT FROM AUTHOR]

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.

Specialty drugs treat complex, severe diseases and offer significant therapeutic advances. Despite their potential to transform patient care, little is known about the drivers of their diffusion. In this study, the authors develop a framework for the diffusion of specialty drugs that is motivated by the drugs' novelty, complexity, and importance, with a focus on the role of scientific evidence production. They propose that the effects of scientific evidence on specialty drug diffusion are multifaceted and generated through the three stages of the scientific evidence production process: unpublished clinical studies, publications in medical journals not cited in clinical guidelines, and clinical guidelines. The findings from the empirical analysis of two specialty drugs validate the framework, supporting the idea of multistage scientific evidence effects. In contrast, marketing activities do not have a significant effect. Although this could be attributed to specialty drug prescribers discounting information from commercial sources, it may also be due to limited marketing support. An additional analysis on a nonspecialty drug further validates the proposed framework. Calculations of scientific evidence contributions to trial prescriptions indicate that scientific evidence production can generate returns beyond the publication stage, which should provide specialty drug manufacturers with strong incentives to commit to quality and innovation. [ABSTRACT FROM AUTHOR]

Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institutes L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Atherosclerotic coronary artery disease (CAD) is the causal pathological process driving most major adverse cardiovascular events (MACE) worldwide. The complex development of atherosclerosis manifests as intimal plaque which occurs in the presence or absence of traditional risk factors. There are numerous effective medications for modifying CAD but new pharmacologic therapies require increasingly large and expensive cardiovascular outcome trials to assess their potential impact on MACE and to obtain regulatory approval. For many disease areas, nearly a half of drugs are approved by the U.S. Food & Drug Administration based on beneficial effects on surrogate endpoints. For cardiovascular disease, only low-density lipoprotein cholesterol and blood pressure are approved as surrogates for cardiovascular disease. Valid surrogates of CAD are urgently needed to facilitate robust evaluation of novel, beneficial treatments and inspire investment. Fortunately, advances in non-invasive imaging offer new opportunity for accelerating CAD drug development. Coronary computed tomography angiography (CCTA) is the most advanced candidate, with the ability to measure accurately and reproducibly characterize the underlying causal disease itself. Indeed, favourable changes in plaque burden have been shown to be associated with improved outcomes, and CCTA may have a unique role as an effective surrogate endpoint for therapies that are designed to improve CAD outcomes. CCTA also has the potential to de-risk clinical endpoint-based trials both financially and by enrichment of participants at higher likelihood of MACE. Furthermore, total non-calcified, and high-risk plaque volume, and their change over time, provide a causally linked measure of coronary artery disease which is inextricably linked to MACE, and represents a robust surrogate imaging biomarker with potential to be endorsed by regulatory authorities. Global consensus on specific imaging endpoints and protocols for optimal clinical trial design is essential as we work towards a rigorous, sustainable and staged pathway for new CAD therapies.

Autophagy is a form of programmed cell degradation that enables the maintenance of homeostasis in response to extracellular stress stimuli. Autophagy is primarily activated by starvation and mediates the degradation, removal, or recycling of cell cytoplasm, organelles, and intracellular components in eukaryotic cells. Autophagy is also involved in the pathogenesis of human diseases, including several cancers. Human uveal melanoma (UM) is the primary intraocular malignancy in adults and has an extremely poor prognosis; at present there are no effective therapies. Several studies have suggested that autophagy is important in UM. By understanding the mechanisms of activation of autophagy in UM it may be possible to develop biomarkers to provide more definitive disease prognoses and to identify potential drug targets for the development of new therapeutic strategies. This article reviews the current information regarding autophagy in UM that could facilitate biomarker and drug development.

In theory, financial markets promote innovation by selectively allocating capital to high-quality projects. In this article, I show that equity markets can also inhibit innovation. In public firms, I find that short-term equity market declines cause pharmaceutical companies to abandon early-stage drug developments, irrespective of drug quality or changes in a firm's stock price. I show that financing constraints drive this behavior, highlighting that even short-term market fluctuations can have long-term effects on pharmaceutical innovation and prevent potentially life-saving drugs from progressing to the market. [ABSTRACT FROM AUTHOR]

Each year, approximately 50,000 children under 5 die as a result of diarrhea caused by Cryptosporidium parvum, a protozoan parasite. There are currently no effective drugs or vaccines available to cure or prevent Cryptosporidium infection, and there are limited tools for identifying and validating targets for drug or vaccine development. We previously reported a high throughput screening (HTS) of a large compound library against Plasmodium N-myristoyltransferase (NMT), a validated drug target in multiple protozoan parasite species. To identify molecules that could be effective against Cryptosporidium, we counter-screened hits from the Plasmodium NMT HTS against Cryptosporidium NMT. We identified two potential hit compounds and validated them against CpNMT to determine if NMT might be an attractive drug target also for Cryptosporidium. We tested the compounds against Cryptosporidium using both cell-based and NMT enzymatic assays. We then determined the crystal structure of CpNMT bound to Myristoyl-Coenzyme A (MyrCoA) and structures of ternary complexes with MyrCoA and the hit compounds to identify the ligand binding modes. The binding site architectures display different conformational states in the presence of the two inhibitors and provide a basis for rational design of selective inhibitors.

The emergence and spread of drug-recalcitrant Plasmodium falciparum parasites threaten to reverse the gains made in the fight against malaria. Urgent measures need to be taken to curb this impending challenge. The higher plant-derived sesquiterpene, quinoline alkaloids, and naphthoquinone natural product classes of compounds have previously served as phenomenal chemical scaffolds from which integral antimalarial drugs were developed. Historical successes serve as an inspiration for the continued investigation of plant-derived natural products compounds in search of novel molecular templates from which new antimalarial drugs could be developed. The aim of this study was to identify potential chemical scaffolds for malaria drug discovery following analysis of historical data on phytochemicals screened in vitro against P. falciparum. To identify these novel scaffolds, we queried an in-house manually curated database of plant-derived natural product compounds and their in vitro biological data. Natural products were assigned to different structural classes using NPClassifier. To identify the most promising chemical scaffolds, we then correlated natural compound class with bioactivity and other data, namely (i) potency, (ii) resistance index, (iii) selectivity index and (iv) physicochemical properties. We used an unbiased scoring system to rank the different natural product classes based on the assessment of their bioactivity data. From this analysis we identified the top-ranked natural product pathway as the alkaloids. The top three ranked super classes identified were (i) pseudoalkaloids, (ii) naphthalenes and (iii) tyrosine alkaloids and the top five ranked classes (i) quassinoids (of super class triterpenoids), (ii) steroidal alkaloids (of super class pseudoalkaloids) (iii) cycloeudesmane sesquiterpenoids (of super class triterpenoids) (iv) isoquinoline alkaloids (of super class tyrosine alkaloids) and (v) naphthoquinones (of super class naphthalenes). Launched chemical space of these identified classes of compounds was, by and large, distinct from that of legacy antimalarial drugs. Our study was able to identify chemical scaffolds with acceptable biological properties that are structurally different from current and previously used antimalarial drugs. These molecules have the potential to be developed into new antimalarial drugs.

Answering challenging questions in drug development sometimes requires pharmacokinetic (PK) data analysis across different studies, for example, to characterize PKs across diverse regions or populations, or to increase statistical power for subpopulations by combining smaller size trials. Given the growing interest in data sharing and advanced computational methods, knowledge integration based on multiple data sources is increasingly applied in the context of model-informed drug discovery and development. A powerful analysis method is the individual patient data meta-analysis (IPDMA), leveraging systematic review of databases and literature, with the most detailed data type of the individual patient, and quantitative modeling of the PK processes, including capturing heterogeneity of variance between studies. The methodology that should be used in IPDMA in the context of population PK analysis is summarized in this tutorial, highlighting areas of special attention compared to standard PK modeling, including hierarchical nested variability terms for interstudy variability, and handling between-assay differences in limits of quantification within a single analysis. This tutorial is intended for any pharmacological modeler who is interested in performing an integrated analysis of PK data across different studies in a systematic and thorough manner, to answer questions that transcend individual primary studies.

Purpose of reviewInnovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians.Recent findingsPhase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

PARP inhibitors in ovarian cancer have been a breakthrough therapy of the past decade, driven by positive trial results, and supported by an original pharmacological rationale. However, with mature data, detrimental survival results led to the withdrawals, in 2022, of all approved PARP inhibitors in the most advanced settings' indication (as monotherapy in third or subsequent lines) by the US Food and Drug Administration (FDA). Two other indications, as maintenance after relapse, were also restricted. In this work, based on pooled meta-analysis in each setting, we question a unique situation in oncology: a survival benefit is seen in front-line settings, with, at the same time, a survival decrement in later lines. Either this original feature is explained by the unique biological action of PARP inhibitors-through synthetic lethality-in patients with ovarian cancer and homologous repair deficiency. Another explanation may be trial design: decrement in later lines could partly explain why beneficial results were seen in early settings, simply by avoiding late exposure to PARP inhibitors in the experimental arm in those trials. High crossover rates seen in some trials further support this alternate hypothesis. We contend our analysis and recent survival results of PARP inhibitors warrant a whole reassessment of the place of these compounds in the landscape of ovarian cancer treatments.

Target deconvolution is a crucial but costly and time-consuming task that hinders large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we performed target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed that drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A2 as potential off-targets of bafetinib and abemaciclib, respectively. This work represents the largest thermal profiling of structurally diverse drugs across multiple cell lines to date.

SLE is a complex autoimmune disease with considerable unmet need. Numerous clinical trials designed to investigate novel therapies are actively enrolling patients straining limited resources and creating inefficiencies that increase enrolment challenges. This has motivated investigators developing novel drugs and treatment strategies to consider innovative trial designs that aim to improve the efficiency of generating evidence; these strategies propose conducting fewer trials, involving smaller numbers of patients, while maintaining scientific rigour in safety and efficacy data collection and analysis. In this review we present the design of two innovative phase IIb studies investigating efavaleukin alfa and rozibafusp alfa for the treatment of SLE which use an adaptive study design. This design was selected as a case study, investigating efavaleukin alfa, in the Food and Drug Administrations Complex Innovative Trial Design Pilot Program. The adaptive design approach includes prospectively planned modifications at predefined interim timepoints. Interim assessments of futility allow for a trial to end early when the investigational therapy is unlikely to provide meaningful treatment benefits to patients, which can release eligible patients to participate in other-potentially more promising-trials, or seek alternative treatments. Response-adaptive randomisation allows randomisation ratios to change based on accumulating data, in favour of the more efficacious dose arm(s), while the study is ongoing. Throughout the trial the placebo arm allocation ratio is maintained constant. These design elements can improve the statistical power in the estimation of treatment effect and increase the amount of safety and efficacy data collected for the optimal dose(s). Furthermore, these trials can provide the required evidence to potentially serve as one of two confirmatory trials needed for regulatory approval. This can reduce the need for multiple phase III trials, the total patient requirements, person-exposure risk, and ultimately the time and cost of investigational drug development programmes.

Tauopathies are disorders associated with tau protein dysfunction and insoluble tau accumulation in the brain at autopsy. Multiple lines of evidence from human disease, as well as nonclinical translational models, suggest that tau has a central pathologic role in these disorders, historically thought to be primarily related to tau gain of toxic function. However, a number of tau-targeting therapies with various mechanisms of action have shown little promise in clinical trials in different tauopathies. We review what is known about tau biology, genetics, and therapeutic mechanisms that have been tested in clinical trials to date. We discuss possible reasons for failures of these therapies, such as use of imperfect nonclinical models that do not predict human effects for drug development; heterogeneity of human tau pathologies which may lead to variable responses to therapy; and ineffective therapeutic mechanisms, such as targeting of the wrong tau species or protein epitope. Innovative approaches to human clinical trials can help address some of the difficulties that have plagued our field's development of tau-targeting therapies thus far. Despite limited clinical success to date, as we continue to refine our understanding of tau's pathogenic mechanism(s) in different neurodegenerative diseases, we remain optimistic that tau-targeting therapies will eventually play a central role in the treatment of tauopathies.

The omnipresence and diversity of allosteric regulation in proteins and protein associations complemented by the potential for the design of allosterically acting biologics and drugs call for the development of a new generation of computational models for the analysis of allostery and rational engineering/design of desired signaling and effector molecules determining it. One of the most important challenges is the consideration of the role of amino acid sequence in forming the protein's allosteric communication, including the mode and strength of the allosteric signal that is communicated to the regulated functional site. Here, we present the network-based model with a sequence dependence added in consideration of allosteric communication by combining the structure-based statistical mechanical model of allostery with the Miyazawa-Jernigan residue–residue potential. Applying the model in the analysis of five classical allosteric proteins, we found that it is necessary to consider the following two major determinants: (i) the free energy exerted by the allosteric site on the regulated one and (ii) the background (average) change in dynamics of the overall structure. We show that working together these two components determine the allosteric modulation, calling one to study their dependence on structures, oligomerization states, and sequence divergence in different proteins. [ABSTRACT FROM AUTHOR]

Synthesis of bicyclic scaffolds has emerged as an important research topic in modern drug development because they can serve as saturated bioisosters to enhance the physicochemical properties and metabolic profiles of drug candidates. Here we report a remarkably simple silver‐enabled strategy to access polysubstituted 3‐azabicyclo[3.1.1]heptanes in a single operation from readily accessible bicyclobutanes (BCBs) and isocyanides. The process is proposed to involve a formal (3+3)/(3+2)/retro‐(3+2) cycloaddition sequence. This novel protocol allows for rapid generation of molecular complexity from simple starting materials, and the products can be easily derivatized, further enriching the BCB cycloaddition chemistry and the growing set of valuable sp3‐rich bicyclic building blocks. [ABSTRACT FROM AUTHOR]

Accurate identification of potential drug-target pairs is a crucial step in drug development and drug repositioning, which is characterized by the ability of the drug to bind to and modulate the activity of the target molecule, resulting in the desired therapeutic effect. As machine learning and deep learning technologies advance, an increasing number of models are being engaged for the prediction of drug-target interactions. However, there is still a great challenge to improve the accuracy and efficiency of predicting. In this study, we proposed a deep learning method called Multi-source Information Fusion and Attention Mechanism for Drug-Target Interaction (MIFAM-DTI) to predict drug-target interactions. Firstly, the physicochemical property feature vector and the Molecular ACCess System molecular fingerprint feature vector of a drug were extracted based on its SMILES sequence. The dipeptide composition feature vector and the Evolutionary Scale Modeling -1b feature vector of a target were constructed based on its amino acid sequence information. Secondly, the PCA method was employed to reduce the dimensionality of the four feature vectors, and the adjacency matrices were constructed by calculating the cosine similarity. Thirdly, the two feature vectors of each drug were concatenated and the two adjacency matrices were subjected to a logical OR operation. And then they were fed into a model composed of graph attention network and multi-head self-attention to obtain the final drug feature vectors. With the same method, the final target feature vectors were obtained. Finally, these final feature vectors were concatenated, which served as the input to a fully connected layer, resulting in the prediction output. MIFAM-DTI not only integrated multi-source information to capture the drug and target features more comprehensively, but also utilized the graph attention network and multi-head self-attention to autonomously learn attention weights and more comprehensively capture information in sequence data. Experimental results demonstrated that MIFAM-DTI outperformed state-of-the-art methods in terms of AUC and AUPR. Case study results of coenzymes involved in cellular energy metabolism also demonstrated the effectiveness and practicality of MIFAM-DTI. [ABSTRACT FROM AUTHOR]

Chronic pain and opioid use disorder (OUD) are major public health problems, with rising opioid‐related overdose deaths linked to increased opioid prescriptions for pain management. Novel treatment approaches for these commonly comorbid disorders are needed. Growing evidence supports a role for glial activation for both chronic pain and substance use disorders, including OUD. This review provides an overview of glial modulators as a novel treatment approach for comorbid pain and OUD. We aim to synthesize clinical studies investigating the efficacy of glial modulators in treating these comorbid disorders. We conducted a literature search of PubMed and Google Scholar databases in October 2023 to identify relevant clinical trials. The included studies varied in terms of patient population, study methodology and outcomes assessed, and were often limited by small sample sizes and other methodological issues. Additionally, several glial modulators have yet to be studied for chronic pain and OUD. Despite these limitations, these studies yielded positive signals that merit further investigation. Both chronic pain and OUD remain significant public health problems, with many treatment challenges. Glial modulators continue to hold promise as novel therapeutics for comorbid pain and OUD, given positive indications that they can improve pain measures, and reduce addiction‐related outcomes. As our understanding of the mechanisms underlying the contributions of glial modulators to pain and addiction behaviours deepens, we will be better equipped to identify more specific therapeutic targets for chronic pain and OUD. [ABSTRACT FROM AUTHOR]

Ten years ago, the second messenger cGAMP was discovered as the activator of the anti-cancer STING pathway. The characterization of cGAMP's paracrine action and dominant extracellular hydrolase ENPP1 cemented cGAMP as an intercellular immunotransmitter that coordinates the innate and adaptive immune systems to fight cancer. In this Perspective, I look back at a decade of discovery of extracellular cGAMP biology and drug development aiming to supply or preserve extracellular cGAMP for cancer treatment. Reviewing our understanding of the cell type-specific regulatory mechanisms of STING agonists, including their transporters and degradation enzymes, I explain on a molecular and cellular level the successes and challenges of direct STING agonists for cancer therapy. Based on what we know now, I propose new ways to stimulate the STING pathway in a manner that is not only cancer specific, but also cell type specific to fully harness the anti-cancer effect of cGAMP while avoiding collateral damage. Li recounts the characterization of the second messenger cGAMP as a key intercellular innate immune transmitter and development of its pharmacological analogs. Cell type-specific cGAMP transporters, hydrolases, and regulators together tightly regulate the paracrine cGAMP-STING signaling that can be harnessed to activate our powerful anti-cancer immune system. [ABSTRACT FROM AUTHOR]

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein–drug complexes, promote broader QR applications and provide more atomistic insights into drug development. Biomacromolecule structures are vital for drug design. Here, the authors integrate one or two machine learning potentials in an ONIOM-based quantum refinement method, achieving reliable drug structures at an QM level in proteins with high efficiency. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) have the potential to modernize drug development and clinical trial operations by remotely, passively, and continuously collecting ecologically valid evidence that is meaningful to patients' lived experiences. Such evidence holds potential for all drug development stakeholders, including regulatory agencies, as it will help create a stronger evidentiary link between approval of new therapeutics and the ultimate aim of improving patient lives. However, only a very small number of novel digital measures have matured from exploratory usage into regulatory qualification or efficacy endpoints. This shows that despite the clear potential, actually gaining regulatory agreement that a new measure is both fit-for-purpose and delivers value remains a serious challenge. One of the key stumbling blocks for developers has been the requirement to demonstrate that a digital measure is meaningful to patients. This viewpoint aims to examine the co-evolution of regulatory guidance in the United States (U.S.) and best practice for integration of DHTs into the development of clinical outcome assessments. Contextualizing guidance on meaningfulness within the larger shift towards a patient-centric drug development approach, this paper reviews the U.S. Food and Drug Administration (FDA) guidance and existing literature surrounding the development of meaningful digital measures and patient engagement, including the recent examples of rejections by the FDA that further emphasize patient-centricity in digital measures. Finally, this paper highlights remaining hurdles and provides insights into the established frameworks for development and adoption of digital measures in clinical research. [ABSTRACT FROM AUTHOR]

Hearing loss constitutes a major global health concern impacting approximately 1.5 billion people worldwide. Its incidence is undergoing a substantial surge with some projecting that by 2050, a quarter of the global population will experience varying degrees of hearing deficiency. Environmental factors such as aging, exposure to loud noise, and the intake of ototoxic medications are implicated in the onset of acquired hearing loss. Ototoxicity resulting in inner ear damage is a leading cause of acquired hearing loss worldwide. This could be minimized or avoided by early testing of hearing functions in the preclinical phase of drug development. While the assessment of ototoxicity is well defined for drug candidates in the hearing field - required for drugs that are administered by the otic route and expected to reach the middle or inner ear during clinical use - ototoxicity testing is not required for all other therapeutic areas. Unfortunately, this has resulted in more than 200 ototoxic marketed medications. The aim of this publication is to raise awareness of drug-induced ototoxicity and to formulate some recommendations based on available guidelines and own experience. Ototoxicity testing programs should be adapted to the type of therapy, its indication (targeting the ear or part of other medications classes being potentially ototoxic), and the number of assets to test. For multiple molecules and/or multiple doses, screening options are available: in vitro (otic cell assays), ex vivo (cochlear explant), and in vivo (in zebrafish). In assessing the ototoxicity of a candidate drug, it is good practice to compare its ototoxicity to that of a wellknown control drug of a similar class. Screening assays provide a streamlined and rapid method to know whether a drug is generally safe for inner ear structures. Mammalian animal models provide a more detailed characterization of drug ototoxicity, with a possibility to localize and quantify the damage using functional, behavioral, and morphological read-outs. Complementary histological measures are routinely conducted notably to quantify hair cells loss with cochleogram. Ototoxicity studies can be performed in rodents (mice, rats), guinea pigs and large species. However, in undertaking, or at the very least attempting, all preclinical investigations within the same species, is crucial. This encompasses starting with pharmacokinetics and pharmacology efficacy studies and extending through to toxicity studies. In life read-outs include Auditory Brainstem Response (ABR) and Distortion Product OtoAcoustic Emissions (DPOAE) measurements that assess the activity and integrity of sensory cells and the auditory nerve, reflecting sensorineural hearing loss. Accurate, reproducible, and high throughput ABR measures are fundamental to the quality and success of these preclinical trials. As in humans, in vivo otoscopic evaluations are routinely carried out to observe the tympanic membrane and auditory canal. This is often done to detect signs of inflammation. The cochlea is a tonotopic structure. Hair cell responsiveness is position and frequency dependent, with hair cells located close to the cochlea apex transducing low frequencies and those at the base transducing high frequencies. The cochleogram aims to quantify hair cells all along the cochlea and consequently determine hair cell loss related to specific frequencies. This measure is then correlated with the ABR & DPOAE results. Ototoxicity assessments evaluate the impact of drug candidates on the auditory and vestibular systems, de-risk hearing loss and balance disorders, define a safe dose, and optimize therapeutic benefits. These types of studies can be initiated during early development of a therapeutic solution, with ABR and otoscopic evaluations. Depending on the mechanism of action of the compound, studies can include DPOAE and cochleogram. Later in the development, a GLP (Good Laboratory Practice) ototoxicity study may be required based on otic related route of administration, target, or known potential otic toxicity. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a neurodegenerative disease that imposes economic and societal burden. Biomarkers have played a crucial role in the recent approval of aducanumab and lecanemab as disease-modifying therapies which marked a significant milestone for the treatment of AD. The inclusion of biomarkers in AD trials facilitates precise diagnosis, monitors safety, demonstrates target engagement, and supports disease modification. Objective: This study analyzed the utilization state and trends of biomarkers as endpoints in AD trials. Methods: In this retrospective study, trials were collected by searching clinicaltrials.gov using the term "Alzheimer". Primary and secondary outcomes were analyzed separately for each phase. Results: Among the 1,048 analyzed trials, 313 (29.87%) adopted biomarkers as primary endpoints and 364 (34.73%) as secondary endpoints, mainly in phases 1 and 2. The top three biomarkers adopted as primary endpoints in phases 1, 2, and 3 were amyloid-PET, tau-PET, and MRI. The top three biomarkers adopted as secondary endpoints, in phase 1, were cerebrospinal fluid (CSF) amyloid-β (Aβ), blood Aβ and amyloid-PET; in phase 2, they were MRI, CSF Aβ, and CSF phospho-tau; and in phase 3, they were amyloid PET, MRI, and blood Aβ. There was a statistically significant increase in the adoption of biomarkers as primary endpoints in phase 2 trials (p = 0.001) and secondary endpoints in phase 3 trials (p = 0.001). Conclusions: The growing recognition of the importance of biomarkers in AD trial' design and drug development is evident by the significant steady increase in biomarkers' utilization in phases 2 and 3. [ABSTRACT FROM AUTHOR]

Purpose Health-system pharmacists play a crucial role in monitoring the pharmaceutical pipeline to manage formularies, allocate resources, and optimize clinical programs for new therapies. This article aims to support pharmacists by providing periodic updates on new and anticipated novel drug approvals. Summary Selected drug approvals anticipated in the 12-month period covering the first quarter of 2024 through the fourth quarter of 2024 are reviewed. The analysis emphasizes drugs expected to have significant clinical and financial impact in hospitals and clinics, as selected from 59 novel drugs awaiting US Food and Drug Administration approval. This year's pipeline includes recently added drugs with various indications including oncology, infectious diseases, genetic disorders, and rare diseases. New cellular and gene therapies are rapidly evolving and being studied for several rare diseases and cancers. Conclusion More oncology agents, including gene therapies, oral agents, and monoclonal antibodies, are in the pipeline this year. Additional diseases targeted by new novel drugs, including cellular and gene therapies, are hemophilia, nonalcoholic steatohepatitis, Alzheimer's disease, and rare diseases such as galactosemia and epidermolysis bullosa. [ABSTRACT FROM AUTHOR]

Summary: Background: Alpha‐1 antitrypsin liver disease (AATLD) occurs in a subset of patients with alpha‐1 antitrypsin deficiency. Risk factors for disease progression and specific pathophysiologic features are not well known and validated non‐invasive assessments for disease severity are lacking. Currently, there are no approved treatments for AATLD. Aims: To outline existing understanding of AATLD and to identify knowledge gaps critical to improving clinical trial design and development of new treatments. Methods: This report was developed following a multi‐stakeholder forum organised by the Alpha‐1 Antitrypsin Deficiency Related Liver Disease Expert Panel in which experts presented an overview of the available literature on this topic. Results: AATLD results from a 'gain of toxic function' and primarily manifests in those with the homozygous Pi*ZZ genotype. Accumulation of misfolded 'Z' AAT protein in liver cells triggers intracellular hepatocyte injury which may ultimately lead to hepatic fibrosis. Male gender, age over 50 years, persistently elevated liver tests, concomitant hepatitis B or C virus infection, and metabolic syndrome, including obesity and type 2 diabetes mellitus, are known risk factors for adult AATLD. While the gold standard for assessing AATLD disease activity is liver histology, less invasive measures with low intra‐ and inter‐observer variability are needed. Measurement of liver stiffness shows promise; validated thresholds for staging AATLD are in development. Such advances will help patients by enabling risk stratification and personalised surveillance, along with streamlining the development process for novel therapies. Conclusions: This inaugural forum generated a list of recommendations to address unmet needs in the field of AATLD. [ABSTRACT FROM AUTHOR]

Mousedouble minute 2 (MDM2) is one of the molecules activated by p53 and plays an important role in the regulation of p53. MDM2 is generally believed to function as a negative regulator of p53 by facilitating its ubiquitination and subsequent degradation. Consequently, blocked p53 activity often fails in damaged cells to undergo cell cycle arrest or apoptosis. Given that around 50% of human cancers involve the inactivation of p53 through genetic mutations, and directly targeting p53 through drug development has limited feasibility, targeting molecular regulation related to p53 has great potential and has become a research hotspot. For example, developing drugs that target the interaction between p53 and MDM2. Such drugs aim to reactivate p53 by targeting either MDM2 binding or p53 phosphorylation. Researchers have identified various compounds that can serve as inhibitors, either by directly binding to MDM2 or by modifying p53 through phosphorylation. Furthermore, a significant correlation exists between the expression of MDM2 in tumors and the effectiveness of immunotherapy, predominantly in the context of immune checkpoint inhibition. This review presents a comprehensive overview of the molecular characteristics of MDM2 and the current state of research on MDM2-targeting inhibitors. It includes a review of the impact of MDM2 targeting on the efficacy of immunotherapy, providing guidance and direction for the development of drugs targeting the p53-MDM2 interaction and optimization of immunotherapy. [ABSTRACT FROM AUTHOR]

Background: The development of resistance by Plasmodium falciparum is a burdening hazard that continues to undermine the strides made to alleviate malaria. As such, there is an increasing need to find new alternative strategies. This study evaluated and validated 2 medicinal plants used in traditional medicine to treat malaria. Methods: Inspired by their ethnobotanical reputation of being effective against malaria, Ziziphus mucronata and Xysmalobium undulutum were collected and sequentially extracted using hexane (HEX), ethyl acetate (ETA), Dichloromethane (DCM) and methanol (MTL). The resulting crude extracts were screened for their anti-malarial and cytotoxic potential using the parasite lactate dehydrogenase (pLDH) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, respectively. This was followed by isolating the active compounds from the DCM extract of Z. mucronata using silica gel chromatography and structural elucidation using spectroscopic techniques (NMR: 1H, 12C, and DEPT). The active compounds were then targeted against P. falciparum heat shock protein 70–1 (PfHsp70-1) using Autodock Vina, followed by in vitro validation assays using ultraviolet–visible (UV–VIS) spectroscopy and the malate dehydrogenase (MDH) chaperone activity assay. Results: The extracts except those of methanol displayed anti-malarial potential with varying IC50 values, Z. mucronata HEX (11.69 ± 3.84 µg/mL), ETA (7.25 ± 1.41 µg/mL), DCM (5.49 ± 0.03 µg/mL), and X. undulutum HEX (4.9 ± 0.037 µg/mL), ETA (17.46 ± 0.024 µg/mL) and DCM (19.27 ± 0.492 µg/mL). The extracts exhibited minimal cytotoxicity except for the ETA and DCM of Z. mucronata with CC50 values of 10.96 and 10.01 µg/mL, respectively. Isolation and structural characterization of the active compounds from the DCM extracts revealed that betulinic acid (19.95 ± 1.53 µg/mL) and lupeol (7.56 ± 2.03 µg/mL) were responsible for the anti-malarial activity and had no considerable cytotoxicity (CC50 > µg/mL). Molecular docking suggested strong binding between PfHsp70-1, betulinic acid (− 6.8 kcal/mol), and lupeol (− 6.9 kcal/mol). Meanwhile, the in vitro validation assays revealed the disruption of the protein structural elements and chaperone function. Conclusion: This study proves that X undulutum and Z. mucronata have anti-malarial potential and that betulinic acid and lupeol are responsible for the activity seen on Z. mucronata. They also make a case for guided purification of new phytochemicals in the other extracts and support the notion of considering medicinal plants to discover new anti-malarials. [ABSTRACT FROM AUTHOR]

Background: During the previous two decades, PET imaging of biopharmaceuticals radiolabeled with zirconium-89 has become a consistent tool in preclinical and clinical drug development and patient selection, primarily due to its advantageous physical properties that allow straightforward radiolabeling of antibodies (89Zr-immuno-PET). The extended half-life of 78.4 h permits flexibility with respect to the logistics of tracer production, transportation, and imaging and allows imaging at later points in time. Additionally, its relatively low positron energy contributes to high-sensitivity, high-resolution PET imaging. Considering the growing interest in radiolabeling antibodies, antibody derivatives, and other compound classes with 89Zr in both clinical and pre-clinical settings, there is an urgent need to acquire valuable recommendations and guidelines towards standardization of labeling procedures. Main body: This review provides an overview of the key aspects of 89Zr-radiochemistry and radiopharmaceuticals. Production of 89Zr, conjugation with the mostly used chelators and radiolabeling strategies, and quality control of the radiolabeled products are described in detail, together with discussions about alternative options and critical steps, as well as recommendations for troubleshooting. Moreover, some historical background on 89Zr-immuno-PET, coordination chemistry of 89Zr, and future perspectives are provided. This review aims to serve as a quick-start guide for scientists new to the field of 89Zr-immuno-PET and to suggest approaches for harmonization and standardization of current procedures. Conclusion: The favorable PET imaging characteristics of 89Zr, its excellent availability due to relatively simple production and purification processes, and the development of suitable bifunctional chelators have led to the widespread use of 89Zr. The combination of antibodies and 89Zr, known as 89Zr-immuno-PET, has become a cornerstone in drug development and patient selection in recent years. Despite the advanced state of 89Zr-immuno-PET, new developments in chelator conjugation and radiolabeling procedures, application in novel compound classes, and improved PET scanner technology and quantification methods continue to reshape its landscape towards improving clinical outcomes. [ABSTRACT FROM AUTHOR]

The Chinese Trademark Law does not entirely exclude the possibility of generic names being registered as trademarks, which is inconsistent with the international standards. The necessity and rationality of allowing the trademark registration of drug names are worth of further research. This study analyzes the rules and cases that involve the trademark registration of drug names and makes comparisons with international counterparts. It displays that in Chinese judicial practices, drug name listed in the National Drug Standards may be registered as a trademark if it has acquired distinctive characteristics by use, which contradicts the nature of generic names and may exert an anticompetitive effect and have adverse implications on the development of the related drug industry and patient well-being. This study proposes that the drug name sign is different from descriptive sign, and cannot obtain distinctiveness through use. Based on the particularity of the drug industry, the trademark registration of drug names in the National Drug Standards should be prohibited. This arrangement is conductive to addressing the imbalance of interests among drug operators and safeguarding public health. This study can provide insights and policy recommendations for Chinese lawmakers offering a framework to reconcile trademark protection with the pharmaceutical industry's unique characteristics and regulatory demands. [ABSTRACT FROM AUTHOR]

Nuclear receptors are ligand-activated transcription factors that can often be useful drug targets. Unfortunately, ligand promiscuity leads to two-thirds of receptors remaining clinically untargeted. PXR is a nuclear receptor that can be activated by diverse compounds to elevate metabolism, negatively impacting drug efficacy and safety. This presents a barrier to drug development because compounds designed to target other proteins must avoid PXR activation while retaining potency for the desired target. This problem could be avoided by using PXR antagonists, but these compounds are rare, and their molecular mechanisms remain unknown. Here, we report structurally related PXR-selective agonists and antagonists and their corresponding co-crystal structures to describe mechanisms of antagonism and selectivity. Structural and computational approaches show that antagonists induce PXR conformational changes incompatible with transcriptional coactivator recruitment. These results guide the design of compounds with predictable agonist/antagonist activities and bolster efforts to generate antagonists to prevent PXR activation interfering with other drugs. PXR is a receptor activated by diverse compounds that triggers detoxification pathways in the cell, and blocking this receptor may increase the effectiveness of certain drugs. Here, the authors present the structural basis of PXR inhibition. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the leading causes of cancer-related deaths worldwide that presents a substantial peril to human health. Non-Small Cell Lung Cancer (NSCLC) is a main subtype of lung cancer with heightened metastasis and invasion ability. The predominant treatment approaches currently comprise surgical interventions, chemotherapy regimens, and radiotherapeutic procedures. However, it poses significant clinical challenges due to its tumor heterogeneity and drug resistance, resulting in diminished patient survival rates. Therefore, the development of novel treatment strategies for NSCLC is necessary. Ferroptosis was characterized by iron-dependent lipid peroxidation and the accumulation of lipid reactive oxygen species (ROS), leading to oxidative damage of cells and eventually cell death. An increasing number of studies have found that exploiting the induction of ferroptosis may be a potential therapeutic approach in NSCLC. Recent investigations have underscored the remarkable potential of natural products in the cancer treatment, owing to their potent activity and high safety profiles. Notably, accumulating evidences have shown that targeting ferroptosis through natural compounds as a novel strategy for combating NSCLC holds considerable promise. Nevertheless, the existing literature on comprehensive reviews elucidating the role of natural products inducing the ferroptosis for NSCLC therapy remains relatively sparse. In order to furnish a valuable reference and support for the identification of natural products inducing ferroptosis in anti-NSCLC therapeutics, this article provided a comprehensive review explaining the mechanisms by which natural products selectively target ferroptosis and modulate the pathogenesis of NSCLC. [ABSTRACT FROM AUTHOR]

Gynecological cancers pose a significant threat to women's health. Although the pathogenesis of gynecological cancer remains incompletely understood, angiogenesis is widely acknowledged as a fundamental pathological mechanism driving tumor cell growth, invasion, and metastasis. Targeting angiogenesis through natural products has emerged as a crucial strategy for treating gynecological cancer. In this review, we conducted comprehensive searches in PubMed, Embase, Web of Science, Science Direct, and CNKI databases from the first publication until May 2023 to identify natural products that target angiogenesis in gynecologic tumors. Our findings revealed 63 natural products with anti-angiogenic activity against gynecological cancer. These results underscore the significance of these natural products in augmenting their anticancer effects by modulating other factors within the tumor microenvironment via their impact on angiogenesis. This article focuses on exploring the potential of natural products in targeting blood vessels within gynecological cancer to provide novel research perspectives for targeted vascular therapy while laying a solid theoretical foundation for new drug development. [ABSTRACT FROM AUTHOR]