Drug Research And Development Research Articles

Multi-output prediction of dose–response curves enables drug repositioning and biomarker discovery

Drug response prediction is hampered by uncertainty in the measures of response and selection of doses. In this study, we propose a probabilistic multi-output model to simultaneously predict all dose–responses and uncover their biomarkers. By describing the relationship between genomic features and chemical properties to every response at every dose, our multi-output Gaussian Process (MOGP) models enable assessment of drug efficacy using any dose–response metric. This approach was tested across two drug screening studies and ten cancer types. Kullback-leibler divergence measured the importance of each feature and identified EZH2 gene as a novel biomarker of BRAF inhibitor response. We demonstrate the effectiveness of our MOGP models in accurately predicting dose–responses in different cancer types and when there is a limited number of drug screening experiments for training. Our findings highlight the potential of MOGP models in enhancing drug development pipelines by reducing data requirements and improving precision in dose–response predictions.

Shared etiology of Mendelian and complex disease supports drug discovery

BackgroundDrugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease.MethodsIn previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations.ResultsOur analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio = 1.84, p = 5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated.ConclusionsOur findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Characterization of novel double-reporter strains of Mycobacterium abscessus for drug discovery: a study in mScarlet.

Mycobacterium abscessus (Mab) is currently considered an "incurable nightmare." Its intrinsic resistance, high toxicity, long duration, and low cure rates of available therapies often lead to the clinical decision not to treat. Moreover, one of the significant drawbacks of anti-Mab drug development is the lack of correlation between in vitro susceptibility and clinical efficacy. Most drug screening assays are performed on Mab growing in liquid cultures. But being an intracellular pathogen, inducing granulomas and biofilm formation, the broth culture is far from ideal as in vitro drug-testing setup. This study presents new double-reporter Mab strains that allow direct real-time bacterial detection and quantification in a non-invasive way. These strains can be applied to an extensive range of experimental settings, far surpassing the utility of single-reporter bacteria. They can be used in all steps of the pre-clinical anti-Mab drug development pipeline, constituting a highly valuable tool to increase its success.

Enhancing the Intrinsic Antiplasmodial Activity and Improving the Stability and Selectivity of a Tunable Peptide Scaffold Derived from Human Platelet Factor 4.

The control of malaria, a disease caused by Plasmodium parasites that kills over half a million people every year, is threatened by the continual emergence and spread of drug resistance. Therefore, new molecules with different mechanisms of action are needed in the antimalarial drug development pipeline. Peptides developed from host defense molecules are gaining traction as anti-infectives due to theood of inducing drug resistance. Human platelet factor 4 (PF4) has intrinsic activity against P. falciparum, and a macrocyclic helix-loop-helix peptide derived from its active domain recapitulates this activity. In this study, we used a stepwise approach to optimize first-generation PF4-derived internalization peptides (PDIPs) by producing analogues with substitutions to charged and hydrophobic amino acid residues or with modifications to terminal residues including backbone cyclization. We evaluated the in vitro activity of PDIP analogues against P. falciparum compared to their overall helical structure, resistance to breakdown by serum proteases, selective binding to negatively charged membranes, and hemolytic activity. Next, we combined antiplasmodial potency-enhancing substitutions that retained favorable membrane and cell-selective properties onto the most stable scaffold to produce a backbone cyclic PDIP analogue with four-fold improved activity against P. falciparum compared to first-generation peptides. These studies demonstrate the ability to modify PDIP to select for and combine desirable properties and further validate the suitability of this unique peptide scaffold for developing a new molecule class that is distinct from existing antimalarial drugs.

Prediction of Drug-Induced Liver Injury: From Molecular Physicochemical Properties and Scaffold Architectures to Machine Learning Approaches.

The process of developing new drugs is widely acknowledged as being time-intensive and requiring substantial financial investment. Despite ongoing efforts to reduce time and expenses in drug development, ensuring medication safety remains an urgent problem. One of the major problems involved in drug development is hepatotoxicity, specifically known as drug-induced liver injury (DILI). The popularity of new drugs often poses a significant barrier during development and frequently leads to their recall after launch. In silico methods have many advantages compared with traditional invivo and invitro assays. To establish a more precise and reliable prediction model, it is necessary to utilize an extensive and high-quality database consisting of information on drug molecule properties and structural patterns. In addition, we should also carefully select appropriate molecular descriptors that can be used to accurately depict compound characteristics. The aim of this study was to conduct a comprehensive investigation into the prediction of DILI. First, we conducted a comparative analysis of the physicochemical properties of extensively well-prepared DILI-positive and DILI-negative compounds. Then, we used classic substructure dissection methods to identify structural pattern differences between these two different types of chemical molecules. These findings indicate that it is not feasible to establish property or substructure-based rules for distinguishing between DILI-positive and DILI-negative compounds. Finally, we developed quantitative classification models for predicting DILI using the naïve Bayes classifier (NBC) and recursive partitioning (RP) machine learning techniques. The optimal DILI prediction model was obtained using NBC, which combines 21 physicochemical properties, the VolSurf descriptors and the LCFP_10 fingerprint set. This model achieved a global accuracy (GA) of 0.855 and an area under the curve (AUC) of 0.704 for the training set, while the corresponding values were 0.619 and 0.674 for the test set, respectively. Moreover, indicative substructural fragments favorable or unfavorable for DILI were identified from the best naïve Bayesian classification model. These findings may help prioritize lead compounds in the early stage of drug development pipelines.

Uncovering the Elusive Structures and Mechanisms of Prevalent Antidepressants

AbstractMost treatments to alleviate major depression work by either inhibiting human monoamine transporters, vital for the reuptake of monoamine neurotransmitters, or by inhibiting monoamine oxidases, which are vital for their degradation. The analysis of the experimental 3D structures of those antidepressants in their drug formulation state is key to precision drug design and development. In this study, microcrystal electron diffraction (MicroED) is applied to reveal the atomic 3D structures for the first time of five of the most prevalent antidepressants (reboxetine, pipofezine, ansofaxine, phenelzine, and bifemelane) directly from the commercially available powder of the active ingredients. Their modes of binding are investigated by molecular docking, revealing the essential contacts and conformational changes into the biologically active state. This study underscores the combined use of MicroED and molecular docking to uncover elusive drug structures and mechanisms to aid in further drug development pipelines.

Artificial intelligence for ophthalmic drug discovery and development: Capabilities, applications, and challenges

The integration of artificial intelligence (AI) into ophthalmic drug discovery and development presents transformative opportunities to address the inherent complexities and challenges of creating targeted therapies for eye diseases. The ability of AI to process vast datasets can facilitate the discovery of novel drug candidates, improve predictions of drug efficacy and safety, and streamline the drug development pipeline. Applications can range from enhancing target identification and compound screening to refining predictive toxicology. However, challenges such as data limitations, computational demands, model interpretability, and ethical considerations remain. Despite these hurdles, the integration of AI with emerging technologies and its potential to optimize clinical trials signifies a new era of innovation in ophthalmology, emphasizing its critical role in addressing current challenges and advancing therapeutic development. In this paper, we explore the role of AI in ophthalmic drug discovery, highlighting its potential to address critical challenges in the field and delineating its impact across various stages of drug development.

Assessment and process optimization of high throughput biofabrication of immunocompetent breast cancer model for drug screening applications

Recent advancements in 3D cancer modeling have significantly enhanced our ability to delve into the intricacies of carcinogenesis. Despite the pharmaceutical industry’s substantial investment of both capital and time in the drug screening and development pipeline, a concerning trend persists: drug candidates screened on conventional cancer models exhibit a dismal success rate in clinical trials. One pivotal factor contributing to this discrepancy is the absence of drug testing on pathophysiologically biomimetic 3D cancer models during pre-clinical stages. Unfortunately, current manual methods of 3D cancer modeling, such as spheroids and organoids, suffer from limitations in reproducibility and scalability. In our study, we have meticulously developed 3D bioprinted breast cancer model utilizing decellularized adipose tissue-based hydrogel obtained via a detergent-free decellularization method. Our innovative printing techniques allows for rapid, high-throughput fabrication of 3D cancer models in a 96-well plate format, demonstrating unmatched scalability and reproducibility. Moreover, we have conducted extensive validation, showcasing the efficacy of our platform through drug screening assays involving two potent anti-cancer drugs, 5-Fluorouracil and PRIMA-1Met. Notably, our platform facilitates effortless imaging and gene expression analysis, streamlining the evaluation process. In a bid to enhance the relevance of our cancer model, we have introduced a heterogeneous cell population into the DAT-based bioink. Through meticulous optimization and characterization, we have successfully developed a biomimetic immunocompetent breast cancer model, complete with microenvironmental cues and diverse cell populations. This breakthrough paves the way for rapid multiplex drug screening and the development of personalized cancer models, marking a paradigm shift in cancer research and pharmaceutical development.

Real-time label-free three-dimensional invasion assay for anti-metastatic drug screening using impedance sensing.

Tumor metastasis presents a formidable challenge in cancer treatment, necessitating effective tools for anti-cancer drug development. Conventional 2D cell culture methods, while considered the "gold standard" for invasive studies, exhibit limitations in representing cancer hallmarks and phenotypes. This study proposes an innovative approach that combines the advantages of 3D tumor spheroid culture with impedance-based biosensing technologies to establish a high-throughput 3D cell invasion assay for anti-metastasis drug screening through multicellular tumor spheroids. In addition, the xCELLigence device is employed to monitor the time-dependent kinetics of cell behavior, including attachment and invasion out of the 3D matrix. Moreover, an iron chelator (deferoxamine) is employed to monitor the inhibition of epithelial-mesenchymal transition in 3D spheroids across different tumor cell types. The above results indicate that our integrated 3D cell invasion assay with impedance-based sensing could be a promising tool for enhancing the quality of the drug development pipeline by providing a robust platform for predicting the efficacy and safety of anti-metastatic drugs before advancing into preclinical or clinical trials.

Durable complete response and overall survival in patients with heavily pretreated, poor-prognosis non-Hodgkin lymphoma to immunoactivating AVM0703 with few and mild drug-related adverse effects.

7077 Background: In 2022, the majority of new cancer drugs in clinical development are aimed at targets that are already saturated with approved drugs1. This is of concern to patients, payers, sponsor/investors, and regulatory agencies. AVM0703 activates a unique immune response mobilizing an endogenous gamma delta TCR+ and invariant TCR+ bispecific Natural Killer T-like cell (γδTCR+iTCR+NKT) (PCT/US21/19773), that has demonstrated clinical anti-cancer activity against a variety of solid and blood cancers. The potential for AVM0703 to fill a gap in the “no-option” R/R NHL treatment landscape led to the launch of the AVM0703-001 clinical trial (The OPAL Study, NCT04329728). 1) Fougner C et al. Herding in the drug development pipeline. Nat Rev Drug Discov. 2023 Aug;22(8):617-618. Methods: An open-label phase 1 trial evaluating the safety and preliminary efficacy of AVM0703 was completed in patients with hematological malignancies. AVM0703 was administered as a single intravenous (IV) infusion to patients enrolled into a 3+3 dose escalation design. Consolidation therapy of investigator’s choice was allowed, preferably 28 days after AVM0703. For each cohort, 3-6 patients were treated, and dose escalation permitted only after the cohort completed a 7-day dose limiting toxicity (DLT) assessment period with no more than 1 DLT. Patients ≥12 years and >40 kg diagnosed with WHO classified lymphoid neoplasms with R/R disease ineligible for transplant or CAR-T were included. The database has not been formally locked and a cutoff date of Dec 13, 2023, is used for presentation of these Phase 1 results. Results: In summary, 17 patients, 39-86 years of age (median 64) with a median of 4 prior lines (range: 1 to 10) received AVM0703 at 6, 9, 12, 18 and 21 mg/kg. Fifteen (88%) were poor prognosis and 4 had CNS involvement. Only 13 of 17 (76.5%) experienced study-drug related adverse events (AEs) during the DLT period; 15 AEs were grade 1 (78.9%), 2 were grade 2 (10.5%), and 2 were grade 3 (10.5%). The most common AEs were insomnia (N=5), hyperglycemia (N=3), and pruritis (N=2). The total number of All-grade TEAEs recorded in 17 patients was 114 within 30 days post dose, with 98.2% of grades 1-3. Median overall survival (OS) in all dose cohorts was 13.3 months, with 8 of 17 patients alive as of 13 Dec 2023. The recommended Phase 2 dose (RP2D) was determined as 18 mg/kg. For the 18 mg/kg group (N=6), long term durable responses were obtained in 66% (3 CR, 1 PR) with an additional patient converting to late CR, nine months survival was 100%, and median OS has not been reached with follow up between 15.9 and 27.8 months (83% still alive). No safety signals have been identified. Conclusions: RP2D AVM0703, with consolidation therapy at day 28 median (range 7 to 240 days) showed no concerning safety signals and promising long-term survival. Clinical trial information: NCT04329728 .

Bedaquiline-pretomanid-moxifloxacin-pyrazinamide for drug-sensitive and drug-resistant pulmonary tuberculosis treatment: a phase 2c, open-label, multicentre, partially randomised controlled trial

The current tuberculosis (TB) drug development pipeline is being re-populated with candidates, including nitroimidazoles such as pretomanid, that exhibit a potential to shorten TB therapy by exerting a bactericidal effect on non-replicating bacilli. Based on results from preclinical and early clinical studies, a four-drug combination of bedaquiline, pretomanid, moxifloxacin, and pyrazinamide (BPaMZ) regimen was identified with treatment-shortening potential for both drug-susceptible (DS) and drug-resistant (DR) TB. This trial aimed to determine the safety and efficacy of BPaMZ. We compared 4 months of BPaMZ to the standard 6 months of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) in DS-TB. 6 months of BPaMZ was assessed in DR-TB. SimpliciTB was a partially randomised, phase 2c, open-label, clinical trial, recruiting participants at 26 sites in eight countries. Participants aged 18 years or older with pulmonary TB who were sputum smear positive for acid-fast bacilli were eligible for enrolment. Participants with DS-TB had Mycobacterium tuberculosis with sensitivity to rifampicin and isoniazid. Participants with DR-TB had M tuberculosis with resistance to rifampicin, isoniazid, or both. Participants with DS-TB were randomly allocated in a 1:1 ratio, stratified by HIV status and cavitation on chest radiograph, using balanced block randomisation with a fixed block size of four. The primary efficacy endpoint was time to sputum culture-negative status by 8 weeks; the key secondary endpoint was unfavourable outcome at week 52. A non-inferiority margin of 12% was chosen for the key secondary outcome. Safety and tolerability outcomes are presented as descriptive analyses. The efficacy analysis population contained patients who received at least one dose of medication and who had efficacy data available and had no major protocol violations. The safety population contained patients who received at least one dose of medication. This study is registered with ClinicalTrials.gov (NCT03338621) and is completed. Between July 30, 2018, and March 2, 2020, 455 participants were enrolled and received at least one dose of study treatment. 324 (71%) participants were male and 131 (29%) participants were female. 303 participants with DS-TB were randomly assigned to 4 months of BPaMZ (n=150) or HRZE (n=153). In a modified intention-to-treat (mITT) analysis, by week 8, 122 (84%) of 145 and 70 (47%) of 148 participants were culture-negative on 4 months of BPaMZ and HRZE, respectively, with a hazard ratio for earlier negative status of 2·93 (95% CI 2·17-3·96; p<0·0001). Median time to negative culture (TTN) was 6 weeks (IQR 4-8) on 4 months of BPaMZ and 11 weeks (6-12) on HRZE. 86% of participants with DR-TB receiving 6 months of BPaMZ (n=152) reached culture-negative status by week 8, with a median TTN of 5 weeks (IQR 3-7). At week 52, 120 (83%) of 144, 134 (93%) of 144, and 111 (83%) of 133 on 4 months of BPaMZ, HRZE, and 6 months of BPaMZ had favourable outcomes, respectively. Despite bacteriological efficacy, 4 months of BPaMZ did not meet the non-inferiority margin for the key secondary endpoint in the pre-defined mITT population due to higher withdrawal rates for adverse hepatic events. Non-inferiority was demonstrated in the per-protocol population confirming the effect of withdrawals with 4 months of BPaMZ. At least one liver-related treatment-emergent adverse effect (TEAE) occurred among 45 (30%) participants on 4 months of BPaMZ, 38 (25%) on HRZE, and 33 (22%) on 6 months of BPaMZ. Serious liver-related TEAEs were reported by 20 participants overall; 11 (7%) among those on 4 months of BPaMZ, one (1%) on HRZE, and eight (5%) on 6 months of BPaMZ. The most common reasons for discontinuation of trial treatment were hepatotoxicity (ten participants [2%]), increased hepatic enzymes (nine participants [2%]), QTcF prolongation (three participants [1%]), and hypersensitivity (two participants [<1%]). For DS-TB, BPaMZ successfully met the primary efficacy endpoint of sputum culture conversion. The regimen did not meet the key secondary efficacy endpoint due to adverse events resulting in treatment withdrawal. Our study demonstrated the potential for treatment-shortening efficacy of the BPaMZ regimen for DS-TB and DR-TB, providing clinical validation of a murine model widely used to identify such regimens. It also highlights that novel, treatment-shortening TB treatment regimens require an acceptable toxicity and tolerability profile with minimal monitoring in low-resource and high-burden settings. The increased risk of unpredictable severe hepatic adverse events with 4 months of BPaMZ would be a considerable obstacle to implementation of this regimen in settings with high burdens of TB with limited infrastructure for close surveillance of liver biochemistry. Future research should focus on improving the preclinical and early clinical detection and mitigation of safety issues together and further efforts to optimise shorter treatments. TB Alliance.

Emodin inhibits respiratory syncytial virus entry by interactions with fusion protein.

Respiratory syncytial virus (RSV) fusion (F) protein is essential for facilitating virus entry into host cells, providing a hopeful path for combating viral diseases. However, F protein inhibitors can rapidly select for viral resistance. Thus, discovering new inhibitors of F-protein is necessary to enrich the RSV drug development pipeline. In this study, we screen 25 bioactive compounds from Chinese herbal medicines that exhibit a strong binding to the RSV-F protein using surface plasmon resonance. After screening, we found emodin could strongly bind to RSV-F protein, and could effectively curb RSV infection. Further investigations certificated that emodin specifically disrupts the attachment and internalization phases of RSV infection by targeting the RSV-F protein. In vivo studies with mice infected with RSV demonstrated that emodin effectively reduces lung pathology. This therapeutic effect is attributed to emodin's capacity to diminish pro-inflammatory cytokine production and reduce viral load in the lungs. In conclusion, our findings provide initial insights into the mechanism by which emodin counters RSV infection via engagement with the RSV-F protein, establishing it as a viable contender for the development of novel therapeutic agents aimed at RSV.

Guiding organs-on-chips towards applications: a balancing act between integration of advanced technologies and standardization

Organs-on-chips (OoC) are in vitro models that emulate key functionalities of tissues or organs in a miniaturized and highly controlled manner. Due to their high versatility, OoC have evolved as promising alternatives to animal testing for a more effective drug development pipeline. Additionally, OoC are revealing increased predictive power for toxicity screening applications as well as (patho-) physiology research models. It is anticipated that enabling technologies such as biofabrication, multimodality imaging, and artificial intelligence will play a critical role in the development of the next generation of OoC. These domains are expected to increase the mimicry of the human micro-physiology and functionality, enhance screening of cellular events, and generate high-content data for improved prediction. Although exponentially growing, the OoC field will strongly benefit from standardized tools to upgrade its implementational power. The complexity derived from the integration of multiple technologies and the current absence of concrete guidelines for establishing standards may be the reason for the slower adoption of OoC by industry, despite the fast progress of the field. Therefore, we argue that it is essential to consider standardization early on when using new enabling technologies, and we provide examples to illustrate how to maintain a focus on technology standards as these new technologies are used to build innovative OoC applications. Moreover, we stress the importance of informed design, use, and analysis decisions. Finally, we argue that this early focus on standards in innovation for OoC will facilitate their implementation.

Spectrum of activity and mechanisms of azole-bisphosphonate synergy in pathogenic Candida.

Candida is a common and often very serious opportunistic fungal pathogen. Invasive candidiasis is a prevalent cause of nosocomial infections with a high mortality rate, and mucocutaneous infections significantly impact the quality of life of millions of patients a year. These infections pose substantial clinical challenges, particularly as the currently available antifungal treatment options are limited in efficacy and often toxic. Azoles are a mainstay of antifungal therapy and work by targeting the biosynthesis of ergosterol. However, there are rising rates of acquired azole resistance in various Candida species, and some species are considered intrinsically resistant to most azoles. Our research demonstrates the promising therapeutic potential of synergistically enhancing azoles with non-toxic, FDA-approved bisphosphonates. Repurposing bisphosphonates as antifungal synergists can bypass much of the drug development pipeline and accelerate the translation of azole-bisphosphonate combination therapy.

QLDTI: A Novel Reinforcement Learning-based Prediction Model for Drug-Target Interaction

Background: Predicting drug-target interaction (DTI) plays a crucial role in drug research and development. More and more researchers pay attention to the problem of developing more powerful prediction methods. Traditional DTI prediction methods are basically realized by biochemical experiments, which are time-consuming, risky, and costly. Nowadays, DTI prediction is often solved by using a single information source and a single model, or by combining some models, but the prediction results are still not accurate enough. Objective: The study aimed to utilize existing data and machine learning models to integrate heterogeneous data sources and different models, further improving the accuracy of DTI prediction. Methods: This paper has proposed a novel prediction method based on reinforcement learning, called QLDTI (predicting drug-target interaction based on Q-learning), which can be mainly divided into two parts: data fusion and model fusion. Firstly, it fuses the drug and target similarity matrices calculated by different calculation methods through Q-learning. Secondly, the new similarity matrices are inputted into five models, NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, for further training. Then, all sub-model weights are continuously optimized again by Q-learning, which can be used to linearly weight all sub-model prediction results to output the final prediction result. Results: QLDTI achieved AUC accuracy of 99.04%, 99.12%, 98.28%, and 98.35% on E, NR, IC, and GPCR datasets, respectively. Compared to the existing five models NRLMF, CMF, BLM-NII, NetLapRLS, and WNN-GIP, the QLDTI method has achieved better results on four benchmark datasets of E, NR, IC, and GPCR. Conclusion: Data fusion and model fusion have been proven effective for DTI prediction, further improving the prediction accuracy of DTI.

Modern Medicine and Pharmaceutics

Modern Medicine and pharmaceuticals have made tremendous strides in enhancing the healthcare and quality of life of people worldwide. This summary explores the key traits and trends in these fields. In recent decades, current medications have witnessed a paradigm shift toward personalized healthcare. Improvements in genomics, proteomics, and record analytics have paved the way for precision medication and tailoring treatments for an individual's genetic makeup. This technique enhances treatment efficacy while minimizing aspect consequences, marking a significant departure from the one-size-fits-all approach. Pharmaceutical research has experienced a revolution with the advent of biotechnology. Biopharmaceuticals, along with monoclonal antibodies and gene treatment plans, have emerged as powerful tools for the fight against formerly untreatable illnesses. Their ability to target specific molecules or genes has opened new frontiers in cancer treatment, uncommon illnesses, and autoimmune diseases. Furthermore, pharmaceutical enterprises have embraced virtual technologies. AI-pushed drug discovery, in silico medical trials, and telemedicine reshape the drug development pipeline and improve the care of affected persons. In particular, telemedicine has gained prominence, presenting available healthcare offerings and far-flung tracking. However, these advancements have moral and regulatory demands. Privateness issues, information protection, and equitable access to present-day remedies remain essential issues. The need for strong moral frameworks and regulatory oversight is paramount

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds.

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.

Abstract LB435: Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification

Abstract Introduction The development of oncology drugs has a persistently high attrition rate of greater than 95%, requiring the development and implementation of novel models and technologies aimed at better predictive abilities during the preclinical phase. Among these, organoid technologies, which involve propagating tumor stem cells in a near-native state, have proven to markedly advance drug testing with enhanced precision. Our previously developed assay-ready organoid technology has paved the way for large panel organoid screening, allowing organoids to augment the efficiency of drug development pipelines, boost patient-derived model usage in preclinical studies, and facilitate accurate predictions of drug action mechanisms. Here, we present a proof-of-concept (POC) large scale organoid experiment, studying the responses of 50 models to the cytotoxic agent paclitaxel, using next generation sequencing data to analyze the drug mechanism of action (MOA) and identify biomarkers that predict drug responses. Methods A panel of 50 organoid models was characterized for paclitaxel response in 9-dose-response curves by measuring ATP levels after a 5-day exposure. Models were genomically profiled by whole-exome sequencing (n = 47) and whole-transcriptome sequencing (n = 44) to analyze the association between organoid pharmacology data and genomic information and elucidate genes and pathways that may be related with, or predictive of, the treatment responses. We performed biomarker analysis on gene expression, mutation, and copy number alteration levels. Results By assessing organoid sensitivity to paclitaxel, we observed no significant indication-specific difference in organoid responses across 7 cancer types. The 50 models were then categorized into responders (AUC &amp;lt; 1.5; n=15) and non-responders (AUC &amp;gt; 2.6; n=14) for biomarker discovery. Differential expression analysis and pathway activation analysis revealed genes and pathways involved in the cell cycle and DNA replication. DNA recombination differentiates responders from non-responders, while genes involved in epithelial cell proliferation, differentiation, and wound healing are associated with drug resistance. Mutation of proto-oncogene RB Transcriptional Corepressor 1, RB1 (a negative regulator of the cell cycle), was found to be a biomarker enriched in non-responders to paclitaxel, corroborating earlier clinical findings. Using the identified profile, we predicted responses for non-tested models in our biobank and validated our approach. Conclusion Large panel organoid drug screening in combination with biomarker analysis brings great opportunities for increasing drug pipeline efficiency. The POC here presented highlights how such a screen can identify MOAs and identify relevant biomarker profiles to be used in clinical trial patient stratification. Citation Format: Linda Xue, Liza Wijler, Marten Hornsveld, Sheng Guo, Leo Price, Marrit Putker, Ludovic Bourre. Large panel organoid drug testing combined with biomarker analysis: Unveiling prospects for mechanism of action identification and preclinical patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB435.

2022-2023 Drug Updates: Investigational Therapeutics in the Pipeline.

During JADPRO Live 2023, Sandra Cuellar, PharmD, BCOP, FHOPA, FASHP, discussed investigational therapeutic agents in the drug development pipeline. Dr. Cuellar highlighted new drug classes, novel mechanisms of action, and safety profiles that advanced practitioners should be aware of.

Alzheimer's disease drug development pipeline: 2024

New therapies to prevent or delay the onset of symptoms, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. We interrogated clinicaltrials.gov including all clinical trials assessing pharmaceutical therapies for AD active in on January 1, 2024. We used the Common Alzheimer's Disease Research Ontology (CADRO) to classify the targets of therapies in the pipeline. There are 164 trials assessing 127 drugs across the 2024 AD pipeline. There were 48 trials in Phase 3 testing 32 drugs, 90 trials in Phase 2 assessing 81 drugs, and 26 trials in Phase 1 testing 25 agents. Of the 164 trials, 34% (N = 56) assess disease-modifying biological agents, 41% (N = 68) test disease-modifying small molecule drugs, 10% (N = 17) evaluate cognitive enhancing agents, and 14% (N = 23) test drugs for the treatment of neuropsychiatric symptoms. Compared to the 2023 pipeline, there are fewer trials (164vs. 187), fewer drugs (127vs. 141), fewer new chemical entities (88vs. 101), and a similar number of repurposed agents (39vs. 40). In the 2024 Alzheimer's disease drug development pipeline, there are 164 clinical trials assessing 127 drugs.The 2024 Alzheimer's disease drug development pipeline has contracted compared to the 2023 Alzheimer pipeline with fewer trials, fewer drugs, and fewer new chemical entities.Drugs in the Alzheimer's disease drug development pipeline target a wide array of targets; the most common processes targeted include neurotransmitter receptors, inflammation, amyloid, and synaptic plasticity.The total development time for a potential Alzheimer's disease therapy to progress from nonclinical studies to FDA review is approximately 13 years.