Platform For Drug Development Research Articles

A standing platform for cancer drug development using ctDNA-based evidence of recurrence.

The time required to conduct clinical trials limits the rate at which we can evaluate and deliver new treatment options to patients with cancer. New approaches to increase trial efficiency while maintaining rigor would benefit patients, especially in oncology, in which adjuvant trials hold promise for intercepting metastatic disease, but typically require large numbers of patients and many years to complete. We envision a standing platform - an infrastructure to support ongoing identification and trial enrolment of patients with cancer with early molecular evidence of disease (MED) after curative-intent therapy for early-stage cancer, based on the presence of circulating tumour DNA. MED strongly predicts subsequent recurrence, with the vast majority of patients showing radiographic evidence of disease within 18 months. Such a platform would allow efficient testing of many treatments, from small exploratory studies to larger pivotal trials. Trials enrolling patients with MED but without radiographic evidence of disease have the potential to advance drug evaluation because they can be smaller (given high probability of recurrence) and faster (given short time to recurrence) than conventional adjuvant trials. Circulating tumour DNA may also provide a valuable early biomarker of treatment effect, which would allow small signal-finding trials. In this Perspective, we discuss how such a platform could be established.

A Machine Learning Approach to Evaluating the Impact of Natural Oils on Alzheimer’s Disease Progression

Alzheimer’s Disease is among the major chronic neurodegenerative diseases that affects more than 50 million people worldwide. This disease irreversibly destroys memory, cognition, and the overall daily activities which occur mainly among the elderly. Few drugs are approved for Alzheimer’s Disease management despite its high prevalence. To date, the available drugs in the market cannot reverse the damage of neurons caused by the disease leading to the exacerbation of symptoms and possibly death. Medicinal plants are considered a rich source of chemical constituents and have been contributing to modern drug discovery in many therapeutic areas including cancer, infectious, cardiovascular, neurodegenerative and Central Nervous System (CNS) diseases. Moreover, essential oils that are extracted from plant organs have been reported for a wide array of biological activities, and their roles as antioxidants, antiaging, cytotoxic, anti-inflammatory, antimicrobial, and enzyme inhibitory activities. This article highlights the promising potential of plants’ essential oils in the discovery of novel therapeutic options for Alzheimer’s Disease and halting its progression. In this article, 428 compounds were reported from the essential oils isolated from 21 plants. A comparative study is carried out by employing a variety of machine learning techniques, validation, and evaluation metrics, to predict essential oils’ efficacy against Alzheimer’s Disease progression. Extensive experiments on essential oil data suggest that a prediction accuracy of up to 82% can be achieved given the proper data preprocessing, feature selection, and model configuration steps. This study underscores the potential of integrating machine learning with natural product research to prioritize and expedite the identification of bioactive essential oils that could lead to effective therapeutic interventions for Alzheimer’s Disease. Further exploration and optimization of machine learning techniques could provide a robust platform for drug discovery and development, facilitating faster and more efficient screening of potential treatments.

Synthesis of Diverse Allylic Sulfone Derivatives via Sequential Hydroalkoxylation of 1,3-Enynes.

A first metal-free protocol for the synthesis of allylic sulfones featuring aldehyde functionality at the δ-position has been reported. The formation of structurally complex δ,δ-dimethoxy allylic sulfones is enabled by the direct nucleophilic attack of methoxide onto the sulfone-containing 1,3-enynes. The present approach allows facile installation of acetal groups within the allylic sulfone scaffold, providing versatile platforms for further functionalization and drug development.

Abstract PO3-26-06: A self-replicating RNA Precision Immunotherapeutic for Overcoming Resistance to Endocrine Therapy in Estrogen Receptor Positive Breast Cancer (ER+BC)

Abstract Advanced or metastatic ER+HER2- breast cancer (ER+BC) is an incurable disease. Standard 1L therapy (SOC) utilizes endocrine blockade with a nonsteroidal aromatase inhibitor (NSAI) combined with a CDK4/6 inhibitor (CDK4/6i). While most patients respond and disease remains controlled for over 2 years (mPFS ~25-28 months), virtually all patients progress over time, requiring more toxic therapies with decreasing periods of disease control. Prolonging the time that the tumor remains under endocrine control is therefore of significant medical value. The most common molecular drivers of endocrine resistance are well characterized and include mutations in ESR1 and bypass mechanisms in select signaling pathways. RBI-1000 is a self-replicating RNA (srRNA) precision immunotherapeutic to generate robust immunity directed against acquired resistance mutations that develop in ER+ BC in response to endocrine blockade by NSAI. RBI-1000 includes 6 on-target mutations within the estrogen receptor ligand binding domain, and bypass mutations either in the form of activating mutations in PI3K kinase or amplifications of HER2/HER3. Precision immunotherapy (PIO), combined with SOC targeted therapy, educates the patient’s immune system to eliminate tumor cells that express acquired resistance mutations. Thus, the tumor is in a lose-lose situation where it is either eliminated by the targeted therapy, or if it develops acquired resistance mutations to the targeted therapy, it is killed by the PIO. Specifically for endocrine resistance, RBI-1000 PIO offers an attractive therapeutic approach to prevent/delay endocrine resistance specifically, by generating an effective immune response against those tumor cells that express the resistance-associated molecules that arise while on NSAI+CDK4/6i. The ability to encode multiple targets in a single therapeutic srRNA molecule, alongside the prolonged expression of srRNA in host cells (>49 days) creates a versatile and powerful cancer drug development platform. srRNA precision immunotherapeutics are delivered intramuscularly (i.e. a jab in the non-dominant deltoid) and generate an effective immune response via the same pathways and mechanisms as a traditional vaccine. We have demonstrated that this srRNA encapsulated in a lipid nanoparticle primes polyfunctional CD4 and CD8 T cells and antibody responses, leading to tumor growth inhibition and improved survival in a mouse model expressing the targeted acquired resistance mutations. Priming of T cells against acquired mutations is also confirmed in human HLA-transgenic mice. The immune cell-mediated elimination of clones expressing the acquired resistance mutations is predicted to prolong endocrine control of ER+BC, in an analogous manner to small molecule or monoclonal antibody targeted therapies, but with a more favorable dosing and adverse event profile due to precise immunologic targeting and no DDI. RBI-1000 is anticipated to enter clinical studies in the first half of 2024. Citation Format: Zelanna Goldberg, Christian Maine, Gabrielle Dailey, Christine Domingo, Gaelle Picarda, Hunter Little, Annie Chou, Jessica Sparks, Darina Spasova, Shiegeki Miyake-Stoner, Zachary C Hartman, Christopher Rabiola, Erika Crosby, Herbert Lyerly, Nathaniel Wang, Parinaz Aliahmad. A self-replicating RNA Precision Immunotherapeutic for Overcoming Resistance to Endocrine Therapy in Estrogen Receptor Positive Breast Cancer (ER+BC) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-26-06.

Abstract 4248: Patient-derived MTAP-deleted bladder cancer organoid model: A unique platform for drug development

Abstract Introduction: Approximately 30% of advanced bladder cancer (BCa) tumors exhibit MTAP gene deletion, which has been associated with luminal subtype BCa, poor response to chemotherapy, and shorter progression to metastatic disease (De Souza 2023). MTAP deletion has been proposed as a potential biomarker for response to pemetrexed and an emerging class of PRMT5 and MAT2A inhibitors in development for MTAP-deleted tumors. Here, we describe the first patient-derived 3D model of BCa with MTAP deletion and its translational potential as a drug sensitivity platform. Methods: A patient (pt) with metastatic BCa harboring MTAP deletion underwent a pelvic lymph node biopsy. A fresh tumor sample was processed under collagenase/dispase solution for 30 minutes and filtered through a 70-um cell strainer. Cells were cultures in low attachment plates with MammoCult™ Basal Medium (Stem Cell Technologies). After 5 passages and 3D spheroid formation, immunostaining for cytokeratin (CK) 5 and 6, GATA3, CK20, and Ki67 staining were performed to confirm the urothelial origin. The MTAP-del was confirmed by a western blot of the organoid specimen and compared to other BLCa cell lines (UMUC, RT4, J82, and 5637). The viability assays were performed using the CellTiter-Glo® 3D Cell kit. Next-generation sequencing (NGS) of primary BCa tumor revealed deletion of MTAP, CDKN2A, CDKN2B, and mutations in p53, TERT, and KDM6A. Results: The patient organoid sample was positive for the luminal CK5/6 markers and negative for GATA3. Ki67 and CK20 were primarily expressed in the nucleus of the same sample. The organoid retained morphologic and immunohistochemical features of the patient’s primary bladder tumor, showing the same squamous differentiation and similar Ki-67 proliferative index. The organoid displayed no evidence of the MTAP protein by western blot (similar findings with the MTAP-del cell lines UMUC and RT4). The organoid showed sensitivity to pemetrexed [pemetrexed IC50=0.12 uM (other MTAP-del BLCa IC50: 0.1-0.23 uM)] and resistance to gemcitabine and cisplatin [Gem IC50=55 nM (other BLCa IC50: 1-29 nM), Cis IC50=90.5 uM (other BLCa IC50: 2-17 uM). Conclusions: We describe the first patient-derived MTAP-deleted BCa organoid recapitulating the features of the primary tumor. The organoid displayed sensitivity to pemetrexed as described with MTAP-deleted BCa. The organoid represents a unique platform for testing novel therapeutic strategies for MTAP-deleted BCa. Citation Format: Maximilian Schwermann, Benedito Carneiro, Shaolei Lu, Andre De Souza, Matthew Hadfield, Anthony Mega, Galina Lagos, Sari Khaleel, Dragan Golijanin, Sheldon Holder, Praveen Srinivasan, William MacDonald, Andrew George, Lanlan Zhou, Leiqing Zhang, Wafik El-Deiry. Patient-derived MTAP-deleted bladder cancer organoid model: A unique platform for drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4248.

Decellularized porcine kidney-incorporated hydrogels for cell-laden bioprinting of renal cell carcinoma model

More than 90% of kidney cancers are attributed to renal cell carcinoma (RCC), which is however highly resistant to traditional chemotherapy. The challenges met in treating RCC signify an imperative to accelerate the development of new and effective drugs. Preclinical testing has served as a foundation for evaluating potential effectiveness of new drugs, but this endeavor is deeply restricted by the current generation of in vitro two-dimensional culture models, which cannot accurately mimic the tumor microenvironment (TME). Therefore, new in vitro three-dimensional (3D) cell culture models that can better mimic the components and architecture of TME have been developed for preclinical testing, but only a few existing 3D cell culture models can simulate the TME of RCC, representing a limitative obstacle impeding the development of novel drugs for RCC. In this study, we prepared a bioink by mixing porcine kidney decellularized extracellular matrix (dECM) powders with gelatin methacryloyl (GelMA) to bioprint an in vitro 3D cell culture model for RCC. We found that GelMA stability, mechanical properties, and printability were all significantly improved following the addition of the dECM powder. Moreover, cell cultures using ACHN cells suggested that kidney dECM powders significantly improved the cellular proliferation and metastasis via upregulation of markers related to epithelial– mesenchymal transition, along with activation of several cancer progression-related signaling pathways. More importantly, ACHN cells also demonstrated higher resistance to sunitinib under the stimulation of kidney dECM, indicating that GelMA-kidney dECM hydrogels may be an appropriate preclinical model to be used for building an in vitro RCC platform for drug screening and development.

Human Brain Organoids in the Preclinical Phase of Drug Development for Migraine

Developing drugs for brain disorders poses significant hurdles. These challenges stem from the scarcity of optimal models for preclinical drug testing and the often observed lack of translation from preclinical to human clinical trials. Further complexity arises from the specific targeting required in many brain disorders, with drug delivery often impeded by the necessity to cross the blood-brain barrier (BBB). As such, the search for novel and efficient platforms for preclinical drug development is a vibrant area of research. In acknowledgment of the limitations of animal tests - such as the lack of translation owing to species differences - and in alignment with the principles of reduction, refinement, and replacement (3Rs), the scientific community is moving towards promoting animal-free drug development plans. In this context, human brain organoids are rapidly emerging as potential alternatives to traditional methods. These early-stage in vitro models, mirroring in vivo complexities, hold great promise for preclinical drug testing for brain disorders. Stable organoid phenotypes and the uncovering of disease-specific features could soon elevate them to a valuable strategy in pharmaceutical testing for a range of brain disorders. Recent advancements in assay-ready organoid platforms and microfluidic chips present considerable potential for the application of human brain organoids in drug development. This commentary briefly discusses the generation of human brain organoids and their application in drug development with existing examples, focusing on their potential use in preclinical drug development for migraine, a prevalent, complex, and disabling brain disorder. The associated challenges and opportunities will also be outlined.

Real-time monitoring of a 3D blood-brain barrier model maturation and integrity with a sensorized microfluidic device.

A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood-brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional in vivo models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. In vitro models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequency-dependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics in vivo shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely a specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine.

Pathological granuloma fibrosis induced by agar-embedded Mycobacterium abscessus in C57BL/6JNarl mice.

Pulmonary granuloma diseases caused by Mycobacterium abscessus (M. abscessus) have increased in past decades, and drug-resistance in this pathogen is a growing public health concern. Therefore, an animal model of chronic granuloma disease is urgently needed. In this study, M. abscessus embedded within agar beads (agar-AB) was used to develop such a model in C57BL/6JNarl mice. Chronic infection was sustained for at least 3 months after agar-AB infection, visible granulomas spread in the lungs, and giant cells and foamy cells appeared in the granulomas. More importantly, pulmonary fibrosis progressed for 3 months, and collagen fibers were detected by Masson trichrome staining. Further, inducible nitric oxide synthase (iNOS) was highly expressed within the alveolar space, and the fibrosis-mediator transforming growth factor beta (TGF-β) began to be expressed at 1 month. Hypoxia-inducible factor (HIF-1α) expression also increased, which aided in normalizing oxygen partial pressure. Although the transient fibrosis persisted for only 3 months, and the pulmonary structure resolved when the pathogen was cleard, this pulmonary fibrosis model for M. abscessus infection will provide a novel test platform for development of new drugs, regimens, and therapies.

Combining fibril-induced alpha-synuclein aggregation and 6-hydroxydopamine in a mouse model of Parkinson's disease and the effect of cerebral dopamine neurotrophic factor on the induced neurodegeneration.

The existent pre-clinical models of Parkinson's disease do not simultaneously recapitulate severe degeneration of dopamine neurons and the occurrence of alpha-synuclein (aSyn) aggregation in one study system. In this study, we injected aSyn pre-formed fibrils (PFF) and 6-hydroxydopamine (6-OHDA) unilaterally into the striatum of C57BL/6 wild-type male mice at an interval of 2 weeks to induce aggregation of aSyn protein and trigger the loss of dopamine neurons simultaneously in one model and studied the behavioural effects of the combination in these mice. 6-OHDA was tested at three different doses, and 2μg of 6-OHDA combined with PFF-induced aSyn aggregation was found to produce the most optimal disease phenotype. At 14 weeks timepoint, mice injected with a combination of PFF and 6-OHDA sustained significant damage to the nigrostriatal pathway and exhibited aSyn-positive aggregation. Our data suggest that the neurons that formed large aSyn aggregates were particularly vulnerable to 6-OHDA-induced degeneration. We also demonstrate the manifestation of a relatively aggressive pathology in 2- to 4-month-old mice, as compared to younger 7- to 9-week-old ones. Furthermore, cerebral dopamine neurotrophic factor (CDNF) administered intrastriatally rescued dopamine neurons and motor behaviour of the animals to some extent from 6-OHDA toxicity. However, no such effect could be seen in the novel 6-OHDA + PFFs combination model. For the first time, we demonstrate the combined effect of PFF and 6-OHDA simultaneously in one model. We further discuss the scope for further optimizing this combination model to develop it as a promising pre-clinical platform for drug screening and development.

Abstract IA007: Design of next generation precision medicine trials using artificial intelligence

Abstract A better understanding of cancer biology has enabled the development of molecularly targeted therapies matching molecular alterations that induce tumor proliferation and metastases. Over the last decades, dozens of drugs were approved in various cancer types based on molecular alterations, often producing prolonged efficacy. More recently, some molecular alterations were found to be relevent across cancer types, such as NTRK translocations, thereby defining novel ontologies of cancer populations that are no longer defined by their cancer type but only a molecular alteration/signature. Although drug development is being challenged by these novel ontologies, patient stratification remains quite straightforward with unidimentional treatment algorithms (1 drug for 1 alteration, across or within cancer types). However, the relevance of some other alterations present in several cancer types sometimes differs across cancer types, but also among patients with a same cancer type, highlithing the issue of cancer heterogeneity. This later much more frequent situation supports a precision medicine approach that takes into account comprehensive molecular profiling. What I will try to explain during my talk is that the move from stratified medicine to precision medicine, defined as the individual selection of a treatment based on comprehensive molecular profiling, can only be done by using artificial intelligence. In order to illustrate this provocative statemen, I will review some initiatives that have used artificail intelligence in their study designs. I will finish by presenting the SENTIER project (Strategies to Expedite and Navigate Therapeutic Innovations through patient-centric and knowledge-Enhanced Research), an integrated research platform for early drug development that pursues the aim to accelerate drug development by connecting data from different early phase clinical trials in order to accelerate drug development. Citation Format: Christophe Le Tourneau. Design of next generation precision medicine trials using artificial intelligence [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA007.

BODIPY-Based Molecules for Biomedical Applications.

BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-s-indacene) derivatives have attracted attention as probes in applications like imaging and sensing due to their unique properties like (1) strong absorption and emission in the visible and near-infrared regions of the electromagnetic spectrum, (2) strong fluorescence and (3) supreme photostability. They have also been employed in areas like photodynamic therapy. Over the last decade, BODIPY-based molecules have even emerged as candidates for cancer treatments. Cancer remains a significant health issue world-wide, necessitating a continuing search for novel therapeutic options. BODIPY is a flexible fluorophore with distinct photophysical characteristics and is a fascinating drug development platform. This review provides a comprehensive overview of the most recent breakthroughs in BODIPY-based small molecules for cancer or disease detection and therapy, including their functional potential.

Why We Fail: An Analysis of Terminated Interventional Clinical Trials in Lymphoma

Introduction: Clinical trials serve as the cornerstone for advancing therapeutic and clinical strategies in Lymphoma. Learning from prematurely terminated trials provides valuable insights for researchers and drug developers. This study analyses interventional clinical trials in lymphoma terminated between 2010 and 2023, aiming to identify common factors contributing to their failure and improve future research in Lymphoma. Methods The study identified clinical trials in Lymphoma registered in the US between 01/01/2010, and 01/07/2023, using the InovaSight platform. The analysis included trials investigating drugs in phases I-III with at least one recruited patient. Phase 4 studies, early access programs, and trials examining medical devices were excluded, as were trials terminated early due to positive efficacy signals. Natural language processing (NLP) & classification algorithms were used to categorize trials into distinct phases of clinical development and were also applied to identify reasons for trial termination from official registrations and relevant publications. These reasons were then characterized using the InovaSight trial ontology for thematic analysis. Enriching features, including mechanism of action information, design characteristics, target enrolment, and inclusion criteria, were generated and linked for each eligible trial. The analysis was conducted using the InovaSight drug development platform, which employs responsible artificial intelligence to expedite the development of novel therapeutics for haematological malignancies. Results: A total of 306 trials met the inclusion criteria, with phase II trials being most frequently withdrawn (49.3%), followed by phase I (42.5%) and phase III (8.2%). The trials spanned from 2010-01-31 to 2021-12-15. Among terminated studies, single-arm trials were most frequent (64%), followed by two-armed designs (21.2%) and three or more arms (14.7%). A concentration of mechanisms of action were observed across all phases (further described in poster). Reasons for early termination included enrolment issues, sponsor decisions, lack of efficacy, safety concerns, funding withdrawal, investigator-related issues, study irrelevance, drug supply problems, COVID-19, and unspecified reasons. The most common reason for early termination was enrolment issues in Phase I and Phase II studies, and sponsor decisions in Phase III studies. Detailed results are presented in Figure 1. Discussion: Our analysis identified a total of 306 trials that met the inclusion criteria, encompassing different phases of clinical development. Interestingly, phase II trials were the most frequently withdrawn, followed by phase I and phase III trials. This observation suggests that a considerable number of trials face challenges during the intermediate stages of development, which could be attributed to various factors such as safety concerns, lack of efficacy, or financial constraints. Our analysis revealed a concentration of similar mechanisms of action across all trial phases. This finding suggests that certain therapeutic areas or drug targets may be more prone to early termination. Exploring the underlying reasons for this pattern could provide valuable insights for future trial planning and design (This is further elucidated in the full presentation). Among the various reasons for early termination, enrolment issues emerged as the most common factor in both Phase I and Phase II studies. This highlights the critical need for in depth and principled approaches to study feasibility assessment routed in an evidence-based methodology. In contrast, sponsor decisions were the leading cause of termination in Phase III trials. This finding suggests that late-stage trials are more susceptible to decisions made by the trial sponsors, which could be influenced by factors such as financial considerations or market dynamics. The results underscore the importance of careful trial planning, patient recruitment strategies, and effective collaboration between sponsors, investigators, and regulatory bodies. Understanding the challenges and factors contributing to trial termination can aid in optimizing future trial designs, mitigating risks, and improving the efficiency of drug development processes.

The Novel Tetra-Specific Drug C-192, Conjugated Using UniStac, Alleviates Non-Alcoholic Steatohepatitis in an MCD Diet-Induced Mouse Model.

Non-alcoholic steatohepatitis (NASH) is a complex disease resulting from chronic liver injury associated with obesity, type 2 diabetes, and inflammation. Recently, the importance of developing multi-target drugs as a strategy to address complex diseases such as NASH has been growing; however, their manufacturing processes remain time- and cost-intensive and inefficient. To overcome these limitations, we developed UniStac, a novel enzyme-mediated conjugation platform for multi-specific drug development. UniStac demonstrated high conjugation yields, optimal thermal stabilities, and robust biological activities. We designed a tetra-specific compound, C-192, targeting glucagon-like peptide 1 (GLP-1), glucagon (GCG), fibroblast growth factor 21 (FGF21), and interleukin-1 receptor antagonist (IL-1RA) simultaneously for the treatment of NASH using UniStac. The biological activity and treatment efficacy of C-192 were confirmed both in vitro and in vivo using a methionine-choline-deficient (MCD) diet-induced mouse model. C-192 exhibited profound therapeutic efficacies compared to conventional drugs, including liraglutide and dulaglutide. C-192 significantly improved alanine transaminase levels, triglyceride accumulation, and the non-alcoholic fatty liver disease activity score. In this study, we demonstrated the feasibility of UniStac in creating multi-specific drugs and confirmed the therapeutic potential of C-192, a drug that integrates multiple mechanisms into a single molecule for the treatment of NASH.

Automated fabrication of a scalable heart-on-a-chip device by 3D printing of thermoplastic elastomer nanocomposite and hot embossing

The successful translation of organ-on-a-chip devices requires the development of an automated workflow for device fabrication, which is challenged by the need for precise deposition of multiple classes of materials in micro-meter scaled configurations. Many current heart-on-a-chip devices are produced manually, requiring the expertise and dexterity of skilled operators. Here, we devised an automated and scalable fabrication method to engineer a Biowire II multiwell platform to generate human iPSC-derived cardiac tissues. This high-throughput heart-on-a-chip platform incorporated fluorescent nanocomposite microwires as force sensors, produced from quantum dots and thermoplastic elastomer, and 3D printed on top of a polystyrene tissue culture base patterned by hot embossing. An array of built-in carbon electrodes was embedded in a single step into the base, flanking the microwells on both sides. The facile and rapid 3D printing approach efficiently and seamlessly scaled up the Biowire II system from an 8-well chip to a 24-well and a 96-well format, resulting in an increase of platform fabrication efficiency by 17,5000–69,000% per well. The device's compatibility with long-term electrical stimulation in each well facilitated the targeted generation of mature human iPSC-derived cardiac tissues, evident through a positive force-frequency relationship, post-rest potentiation, and well-aligned sarcomeric apparatus. This system's ease of use and its capacity to gauge drug responses in matured cardiac tissue make it a powerful and reliable platform for rapid preclinical drug screening and development.

Structural and functional insights into the enzymatic plasticity of the SARS-CoV-2 NiRAN domain

The enzymatic activity of the SARS-CoV-2 nidovirus RdRp-associated nucleotidyltransferase (NiRAN) domain is essential for viral propagation, with three distinct activities associated with modification of the nsp9N terminus, NMPylation, RNAylation, and deRNAylation/capping via a GDP-polyribonucleotidyltransferase reaction. The latter two activities comprise an unconventional mechanism for initiating viral RNA 5' cap formation, while the role of NMPylation is unclear. The structural mechanisms for these diverse enzymatic activities have not been properly delineated. Here, we determine high-resolution cryoelectron microscopy (cryo-EM) structures of catalytic intermediates for the NMPylation and deRNAylation/capping reactions, revealing diverse nucleotide binding poses and divalent metal ion coordination sites to promote its repertoire of activities. The deRNAylation/capping structure explains why GDP is a preferred substrate for the capping reaction over GTP. Altogether, these findings enhance our understanding of the promiscuous coronaviral NiRAN domain, a therapeutic target, and provide an accurate structural platform for drug development.

Financing repurposed drugs for rare diseases: a case study of Unravel Biosciences

BackgroundWe consider two key challenges that early-stage biotechnology firms face in developing a sustainable financing strategy and a sustainable business model: developing a valuation model for drug compounds, and choosing an appropriate operating model and corporate structure. We use the specific example of Unravel Biosciences—a therapeutics platform company that identifies novel drug targets through off-target mechanisms of existing drugs and then develops optimized new molecules—throughout the paper and explore a specific scenario of drug repurposing for rare genetic diseases.ResultsThe first challenge consists of producing a realistic financial valuation of a potential rare disease repurposed drug compound, in this case targeting Rett syndrome. More generally, we develop a framework to value a portfolio of pairwise correlated rare disease compounds in early-stage development and quantify its risk profile. We estimate the probability of a negative return to be 80.8% for a single compound and 56.1% for a portfolio of 8 drugs. The probability of selling the project at a loss decreases from 79.2% (phase 3) for a single compound to 55.4% (phase 3) for the 8-drug portfolio. For the second challenge, we find that the choice of operating model and corporate structure is crucial for early-stage biotech startups and illustrate this point with three concrete examples.ConclusionsRepurposing existing compounds offers important advantages that could help early-stage biotech startups better align their business and financing issues with their scientific and medical objectives, enter a space that is not occupied by large pharmaceutical companies, and accelerate the validation of their drug development platform.

Preclinical Testing Techniques: Paving the Way for New Oncology Screening Approaches.

Prior to clinical trials, preclinical testing of oncology drug candidates is performed by evaluating drug candidates with in vitro and in vivo platforms. For in vivo testing, animal models are used to evaluate the toxicity and efficacy of drug candidates. However, animal models often display poor translational results as many drugs that pass preclinical testing fail when tested with humans, with oncology drugs exhibiting especially poor acceptance rates. The FDA Modernization Act 2.0 promotes alternative preclinical testing techniques, presenting the opportunity to use higher complexity in vitro models as an alternative to in vivo testing, including three-dimensional (3D) cell culture models. Three-dimensional tissue cultures address many of the shortcomings of 2D cultures by more closely replicating the tumour microenvironment through a combination of physiologically relevant drug diffusion, paracrine signalling, cellular phenotype, and vascularization that can better mimic native human tissue. This review will discuss the common forms of 3D cell culture, including cell spheroids, organoids, organs-on-a-chip, and 3D bioprinted tissues. Their advantages and limitations will be presented, aiming to discuss the use of these 3D models to accurately represent human tissue and as an alternative to animal testing. The use of 3D culture platforms for preclinical drug development is expected to accelerate as these platforms continue to improve in complexity, reliability, and translational predictivity.

Revolutionizing Drug Formulation: Harnessing Artificial Intelligence and Machine Learning for Enhanced Stability, Formulation Optimization, and Accelerated Development

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of pharmaceutical drug formulation has sparked a paradigm shift in the way drug stability is predicted, formulations are optimized, and drug development is expedited. This review delves into the transformative impact of AI and ML techniques on pharmaceutical research and development. It highlights how predictive models driven by AI algorithms are effectively simulating drug degradation pathways and stability profiles, enabling scientists to make informed decisions during formulation design. Moreover, the utilization of ML algorithms to analyze vast datasets has led to the discovery of optimal formulations by identifying critical relationships between formulation variables, excipients, and drug properties. This approach not only reduces experimentation time and costs but also enhances the likelihood of developing robust and effective drug products. Furthermore, AI-powered drug development platforms are shortening the timeline for candidate selection, preclinical evaluations, and clinical trials, thereby accelerating the entire drug development process. This article explores the evolving landscape of AI and ML in drug formulation, discusses challenges, and anticipates future prospects in this transformative field.

Modeling the tumor microenvironment of anaplastic thyroid cancer: an orthotopic tumor model in C57BL/6 mice.

Securing a well-established mouse model is important in identifying and validating new therapeutic targets for immuno-oncology. The C57BL/6 mouse is one of the most fully characterised immune system of any animal and provides powerful platform for immuno-oncology discovery. An orthotopic tumor model has been established using TBP3743 (murine anaplastic thyroid cancer [ATC]) cells in B6129SF1 hybrid mice, this model has limited data on tumor immunology than C57BL/6 inbred mice. This study aimed to establish a novel orthotopic ATC model in C57BL/6 mice and characterize the tumor microenvironment focusing immunity in the model. Adapted TBP3743 cells were generated via in vivo serial passaging in C57BL/6 mice. Subsequently, the following orthotopic tumor models were established via intrathyroidal injection: B6129SF1 mice injected with original TBP3743 cells (original/129), B6129SF1 mice injected with adapted cells (adapted/129), and C57BL/6 mice injected with adapted cells (adapted/B6). The adapted TBP3743 cells de-differentiated but exhibited cell morphology, viability, and migration/invasion potential comparable with those of original cells in vitro. The adapted/129 contained a higher Ki-67+ cell fraction than the original/129. RNA sequencing data of orthotopic tumors revealed enhanced oncogenic properties in the adapted/129 compared with those in the original/129. In contrast, the orthotopic tumors grown in the adapted/B6 were smaller, with a lower Ki-67+ cell fraction than those in the adapted/129. However, the oncogenic properties of the tumors within the adapted/B6 and adapted/129 were similar. Immune-related pathways were enriched in the adapted/B6 compared with those in the adapted/129. Flow cytometric analysis of the orthotopic tumors revealed higher cytotoxic CD8+ T cell and monocytic-myeloid-derived suppressor cell fractions in the adapted/B6 compared with the adapted/129. The estimated CD8+ and CD4+ cell fractions in the adapted/B6 were similar to those in human ATCs but negligible in the original/B6. A novel orthotopic tumor model of ATC was established in C57BL/6 mice. Compared with the original B6129SF1 murine model, the novel model exhibited more aggressive tumor cell behaviours and strong immune responses. We expect that this novel model contributes to the understanding tumor microenvironment and provides the platform for drug development.