Tool For Drug Development Research Articles

Digital health technology derived measures: Biomarkersor clinical outcome assessments?

Digital health technologies (DHTs) present unique opportunities for clinical evidence generation but pose certain challenges. These challenges stem, in part, from existing definitions of drug development tools, which were not created with DHT-derived measures in mind. DHT-derived measures can be leveraged as either clinical outcome assessments (COAs) or as biomarkers since they share properties with both categories of drug development tools. Examples from the literature indicate a variety of applications for DHT-derived data, including capturing disease physiology, symptom tracking, or response to therapies. The distinction between the categorization of DHT-derived measures as COAs or as biomarkers can be very fine, with terminology variability among regulatory authorities. This has significant implications for integration of DHT-derived measures in clinical trials, leading to confusion regarding the evidence required to support these tools' use in drug development. There is a need to amend definitions and create clear evidentiary requirements to support broad adoption of these new and innovative tools. The biopharma industry, the technology sector, consulting businesses, academic researchers, and regulators need a dialogue via multi-stakeholder collaborations to clarify questions around DHT-derived measures, to unify definitions, and to create the foundations for evidentiary package requirements, providing a path forward to predictable results.

Advancements in Research on Constructing Physiological and Pathological Liver Models and Their Applications Utilizing Bioprinting Technology.

In recent decades, significant progress has been made in liver tissue engineering through the use of 3D bioprinting technology. This technology offers the ability to create personalized biological structures with precise geometric design capabilities. The complex and multifaceted nature of liver diseases underscores the need for advanced technologies to accurately mimic the physiological and mechanical characteristics, as well as organ-level functions, of liver tissue in vitro. Bioprinting stands out as a superior option over traditional two-dimensional cell culture models and animal models due to its stronger biomimetic advantages. Through the use of bioprinting, it is possible to create liver tissue with a level of structural and functional complexity that more closely resembles the real organ, allowing for more accurate disease modeling and drug testing. As a result, it is a promising tool for restoring and replacing damaged tissue and organs in the field of liver tissue engineering and drug research. This article aims to present a comprehensive overview of the progress made in liver tissue engineering using bioprinting technology to provide valuable insights for researchers. The paper provides a detailed account of the history of liver tissue engineering, highlights the current 3D bioprinting methods and bioinks that are widely used, and accentuates the importance of existing in vitro liver tissue models based on 3D bioprinting and their biomedical applications. Additionally, the article explores the challenges faced by 3D bioprinting and predicts future trends in the field. The progress of 3D bioprinting technology is poised to bring new approaches to printing liver tissue in vitro, while offering powerful tools for drug development, testing, liver disease modeling, transplantation, and regeneration, which hold great academic and practical significance.

From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints.

This paper examines the use of digital endpoints (DEs) derived from digital health technologies (DHTs), focusing primarily on the specific considerations regarding the determination of meaningful change thresholds (MCT). Using DHTs in drug development is becoming more commonplace. There is general acceptance of the value of DHTs supporting patient-centric trial design, capturing data outside the traditional clinical trial setting, and generating DEs with the potential to be more sensitive to change than conventional assessments. However, the transition from exploratory endpoints to primary and secondary endpoints capable of supporting labeling claims requires these endpoints to be substantive with reproducible population-specific values. Meaningful change represents the amount of change in an endpoint measure perceived as important to patients and should be determined for each digital endpoint and given population under consideration. This paper examines existing approaches to determine meaningful change thresholds and explores examples of these methodologies and their use as part of DE development: emphasizing the importance of determining what aspects of health are important to patients and ensuring the DE captures these concepts of interest and aligns with the overarching endpoint strategy. Examples are drawn from published DE qualification documentation and responses to qualification submissions under review by the various regulatory authorities. It is the hope that these insights will inform and strengthen the development and validation of DEs as drug development tools, particularly for those new to the approaches to determine MCTs.

UPS: Opportunities and challenges for gastric cancer treatment.

Gastric cancer remains the fourth most frequently diagnosed malignancy and the fifth leading cause of cancer-related mortality worldwide owning to the lack of efficient drugs and targets for therapy. Accumulating evidence indicates that UPS, which consists of E1, E2, and E3 enzymes and proteasome, plays an important role in the GC tumorigenesis. The imbalance of UPS impairs the protein homeostasis network during development of GC. Therefore, modulating these enzymes and proteasome may be a promising strategy for GC target therapy. Besides, PROTAC, a strategy using UPS to degrade the target protein, is an emerging tool for drug development. Thus far, more and more PROTAC drugs enter clinical trials for cancer therapy. Here, we will analyze the abnormal expression enzymes in UPS and summarize the E3 enzymes which can be developed in PROTAC so that it can contribute to the development of UPS modulator and PROTAC technology for GC therapy.

Modeling glioblastoma complexity with organoids for personalized treatments.

Glioblastoma (GBM) remains a fatal diagnosis despite the current standard of care of maximal surgical resection, radiation, and temozolomide (TMZ) therapy. One aspect that impedes drug development is the lack of an appropriate model representative of the complexity of patient tumors. Brain organoids derived from cell culture techniques provide a robust, easily manipulatable, and high-throughput model for GBM. In this review, we highlight recent progress in developing GBM organoids (GBOs) with a focus on generating the GBM microenvironment (i.e., stem cells, vasculature, and immune cells) recapitulating human disease. Finally, we also discuss the use of organoids as a screening tool in drug development for GBM.

Chemical Biology Approaches toward Precise Structure Control of IgG-Based Antibody-Oligonucleotide Conjugates.

Antibody-oligonucleotide conjugates (AOCs) are important tools for drug development and biochemical analysis. However, the structural heterogeneity of AOCs synthesized through conventional coupling methods raises reproducibility and safety concerns in clinical trials. To address these issues, different covalent coupling approaches have been developed to synthesize AOCs with precise site-specificity and degree of conjugation. This Concept article categorizes these approaches as linker-free or linker-mediated and provides details on their chemistry and potential applications. Several factors, including site-specificity, conjugation control, accessibility, stability, and efficiency, are highlighted when evaluating the pros and cons of these approaches. The article also discusses the future of AOCs, including the development of better conjugation approaches to ensure stimuli-responsive release and the application of high-throughput methods to facilitate their development.

Generation of human induced pluripotent stem cell line (BCRTi006-A) from a patient with focal segmental glomerulosclerosis disease

Individuals with transient reception potential cation channel 6 (TRPC6) mutation have variable phenotypes, ranging from healthy carriers to focal segmental glomerulosclerosis (FSGS). Human induced pluripotent stem cell (hiPSC) line was generated from the urinary cells of a patient with FSGS with a mutant variant of TRPC6. The cells were reprogrammed with Yamanaka factors (OCT3, SOX2, LIN28, L-MYC, and KLF4) using a commercially available Epi5 Reprogramming Kit. The pluripotency of the hiPSC line was confirmed by the expression of common stem cell markers and by their ability to generate all germ layers in vitro. The line is available and registered in the human pluripotent stem cell registry as BCRTi006-A. The generated line represents a valuable tool for disease modeling and drug development for FSGS.

Ancient DNA as a tool for medical research.

Paleogenomics can help elucidate the genetic basis of modern diseases, including inborn errors of immunity that impair the response to infections, providing a tool for drug development.

Usefulness of a humanized tricellular static transwell blood-brain barrier model as a microphysiological system for drug development applications. - A case study based on the benchmark evaluations of blood-brain barrier microphysiological system.

Microphysiological system (MPS), a new technology for invitro testing platforms, have been acknowledged as a strong tool for drug development. In the central nervous system (CNS), the blood‒brain barrier (BBB) limits the permeation of circulating substances from the blood vessels to the brain, thereby protecting the CNS from circulating xenobiotic compounds. At the same time, the BBB hinders drug development by introducing challenges at various stages, such as pharmacokinetics/pharmacodynamics (PK/PD), safety assessment, and efficacy assessment. To solve these problems, efforts are being made to develop a BBB MPS, particularly of a humanized type. In this study, we suggested minimal essential benchmark items to establish the BBB-likeness of a BBB MPS; these criteria support end users in determining the appropriate range of applications for a candidate BBB MPS. Furthermore, we examined these benchmark items in a two-dimensional (2D) humanized tricellular static transwell BBB MPS, the most conventional design of BBB MPS with human cell lines. Among the benchmark items, the efflux ratios of P-gp and BCRP showed high reproducibility in two independent facilities, while the directional transports meditated through Glut1 or TfR were not confirmed. We have organized the protocols of the experiments described above as standard operating procedures (SOPs). We here provide the SOPs with the flow chart including entire procedure and how to apply each SOP. Our study is important developmental step of BBB MPS towards the social acceptance, which enable end users to check and compare the performance the BBB MPSs.

Artificial Intelligence Applied to clinical trials: opportunities and challenges.

Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities' documents. Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.

Naringenin and Hesperidin as Promising Alternatives for Prevention and Co-Adjuvant Therapy for Breast Cancer.

Citrus (genus Citrus L.) fruits are essential sources of bioactive compounds with antioxidant properties, such as flavonoids. These polyphenolic compounds are divided into subclasses, in which flavanones are the most prominent. Among them, naringenin and hesperidin are emerging compounds with anticancer potential, especially for breast cancer (BC). Several mechanisms have been proposed, including the modulation of epigenetics, estrogen signaling, induction of cell death via regulation of apoptotic signaling pathways, and inhibition of tumor invasion and metastasis. However, this information is sparse in the literature and needs to be brought together to provide an overview of how naringenin and hesperidin can serve as therapeutic tools for drug development and as a successful co-adjuvant strategy against BC. This review detailed such mechanisms in this context and highlighted how naringenin and hesperidin could interfere in BC carcinogenesis and be helpful as potential alternative therapeutic sources for breast cancer treatment.

Selinexor demonstrates anti-tumor efficacy in paired patient-derived xenograft models and hydrogel-embedded histoculture drug sensitivity test of penile cancer.

Penile cancer is a rare malignancy with a poor prognosis, even with various treatment options. Considering the little progress in the study of the pathogenesis and treatment of penile cancer because of the lack of models that mimic the biological properties of the tumor, we have developed a patient-derived xenograft (PDX) model and paired hydrogel-embedded histoculture drug sensitivity test (HDST) to screen for drugs that can inhibit tumors. The increased expression of XPO1, as a key nuclear export protein involved in the transport of various tumor suppressors and cell cycle regulatory proteins, is associated with the prognosis of a variety of tumors [World J Uroly 27(2):141-150, 2009]. Selinexor is an inhibitor of XPO1, which can treat cancers, such as multiple myeloma, gastric cancer, triple-negative breast cancer, and non-small cell carcinoma [Transl Androl Urol 6(5):785-790, 2017; OncoTargets Therapy 13:6405-6416, 2020]. However, whether XPO1 inhibition has a role in penile cancer remains unknown. Therefore, this article used the PDX and HDST models to investigate whether the inhibition of XPO1 has an effect on penile cancer and its underlying mechanism. We used penile cancer tumor tissues to construct a PDX model of penile cancer and paired PDXE model and confirmed the consistency of PDX tumor tissues in source patients. Then, we assessed the ability of Selinexor to inhibit penile cancer tissues in vivo using a PDX model and in vitro by HDST. We also examined the potential mechanism of XPO1 action on penile cancer by IHC and TUNEL. Finally, we assessed the safety of the drug treatment by H&E and biochemical blood analysis. Result showed that the penile cancer PDX model and patient penile cancer tissues were clinically consistent in morphological characteristics and protein expression. In addition, Selinexor could inhibit tumor growth in PDX models and HDST. We found that P53, P21 expression was upregulated; Cyclin D1 expression was downregulated, and apoptosis of tumor cells was increased in the Selinexor-treated PDX model. Moreover, it had no significant effect on liver, kidney, and cardiac function. The PDX model of penile cancer was a powerful tool for penile cancer research and new drug development. It showed that Selinexor can effectively inhibit penile cancer in vitro and in vivo. In addition, XPO1 may affect P53, P21, and Cyclin D1 expression to regulate the growth and apoptosis of penile carcinoma.

Multifaceted Target Specificity Analysis as a Tool in Antimicrobial Drug Development: Type III Pantothenate Kinases as a Case Study.

The research and development of a new antimicrobial drug using a target-based approach raises the question of whether any resulting hits will also show activity against the homologous target in other closely related organisms. While an assessment of the similarities of the predicted interactions between the identified inhibitor and the various targets is an obvious first step in answering this question, no clear and consistent framework has been proposed for how this should be done. Here we developed Multifaceted Target Specificity Analysis (MTSA) and applied it to type III pantothenate kinase (PanKIII ) - an essential enzyme required for coenzyme A biosynthesis in a wide range of pathogenic bacteria - as a case study to establish if targeting a specific organism's PanKIII would lead to a narrow- or broad-spectrum agent. We propose that MTSA is a useful tool and aid for directing new target-based antimicrobial drug development initiatives.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery.

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

Identification of Potential Antitubulin Agents with Anticancer Assets from a Series of Imidazo[1,2-a]quinoxaline Derivatives: In Silico and In Vitro Approaches.

Computer-aided drug design is a powerful and promising tool for drug design and development, with a reduced cost and time. In the current study, we rationally selected a library of 34 fused imidazo[1,2-a]quinoxaline derivatives and performed virtual screening, molecular docking, and molecular mechanics for a lead identification against tubulin as an anticancer molecule. The computational analysis and pharmacophoric features were represented as 1A2; this was a potential lead against tubulin, with a maximized affinity and binding score at the colchicine-binding site of tubulin. The efficiency of this lead molecule was further identified using an in vitro assay on a tubulin enzyme and the anticancer potential was established using an MTT assay. Compound 1A2 (IC50 = 4.33-6.11 µM against MCF-7, MDA-MB-231, HCT-116, and A549 cell lines) displayed encouraging results similar to the standard drug colchicine in these in vitro studies, which further confirmed the effectiveness of CADD in new drug developments. Thus, we successfully applied the utility of in silico techniques to identify the best plausible leads from the fused azaheterocycles.

Concept and application of the probability of pharmacological success (PoPS) as a decision tool in drug development: a position paper

BackgroundIn drug development, few molecules from a large pool of early candidates become successful medicines after demonstrating a favourable benefit-risk ratio. Many decisions are made along the way to continue or stop the development of a molecule. The probability of pharmacological success, or PoPS, is a tool for informing early-stage decisions based on benefit and risk data available at the time.ResultsThe PoPS is the probability that most patients can achieve adequate pharmacology for the intended indication while minimising the number of subjects exposed to safety risk. This probability is usually a function of dose; hence its computation typically requires exposure–response models for pharmacology and safety. The levels of adequate pharmacology and acceptable risk must be specified. The uncertainties in these levels, in the exposure–response relationships, and in relevant translation all need to be identified. Several examples of different indications are used to illustrate how this approach can facilitate molecule progression decisions for preclinical and early clinical development. The examples show that PoPS assessment is an effective mechanism for integrating multi-source data, identifying knowledge gaps, and forcing transparency of assumptions. With its application, translational modelling becomes more meaningful and dose prediction more rigorous. Its successful implementation calls for early planning, sound understanding of the disease-drug system, and cross-discipline collaboration. Furthermore, the PoPS evolves as relevant knowledge grows.ConclusionThe PoPS is a powerful evidence-based framework to formally capture multiple uncertainties into a single probability term for assessing benefit-risk ratio. In GSK, it is now expected for governance review at all early-phase decision gates.

Nano-Hydroxyapatite/PLGA Mixed Scaffolds as a Tool for Drug Development and to Study Metastatic Prostate Cancer in the Bone.

(1) Background: Three-dimensional (3D) in vitro, biorelevant culture models that recapitulate cancer progression can help elucidate physio-pathological disease cues and enhance the screening of more effective therapies. Insufficient research has been conducted to generate in vitro 3D models to replicate the spread of prostate cancer to the bone, a key metastatic site of the disease, and to understand the interplay between the key cell players. In this study, we aim to investigate PLGA and nano-hydroxyapatite (nHA)/PLGA mixed scaffolds as a predictive preclinical tool to study metastatic prostate cancer (mPC) in the bone and reduce the gap that exists with traditional 2D cultures. (2) Methods: nHA/PLGA mixed scaffolds were produced by electrospraying, compacting, and foaming PLGA polymer microparticles, +/- nano-hydroxyapatite (nHA), and a salt porogen to produce 3D, porous scaffolds. Physicochemical scaffold characterisation together with an evaluation of osteoblastic (hFOB 1.19) and mPC (PC-3) cell behaviour (RT-qPCR, viability, and differentiation) in mono- and co-culture, was undertaken. (3) Results: The results show that the addition of nHA, particularly at the higher-level impacted scaffolds in terms of mechanical and degradation behaviour. The nHA 4 mg resulted in weaker scaffolds, but cell viability increased. Qualitatively, fluorescent imaging of cultures showed an increase in PC-3 cells compared to osteoblasts despite lower initial PC-3 seeding densities. Osteoblast monocultures, in general, caused an upregulation (or at least equivalent to controls) in gene production, which was highest in plain scaffolds and decreased with increases in nHA. Additionally, the genes were downregulated in PC3 and co-cultures. Further, drug toxicity tests demonstrated a significant effect in 2D and 3D co-cultures. (4) Conclusions: The results demonstrate that culture conditions and environment (2D versus 3D, monoculture versus co-culture) and scaffold composition all impact cell behaviour and model development.

Pharma challenges to adoption of microphysiological system in drug research and development, especially safety assessment

In the environment surrounding the pharma industries (1) declining productivity in research and development in pharma industries, (2) spreading of 3Rs in animal testing, and (3) diversifying of modalities, the regulatory authorities and pharmaceutical companies in US and EU are paying attention to the microphysiological system (MPS) to actively incorporate cutting-edge technologies into the evaluation assays used for approval applications for drug research and development. MPS generally refers to an in vitro culture system in which a culture environment close to that of a living body (in vivo) is reconstructed in a created minute space using a microfluidic device. Currently, many products have been put into practical use by US and EU companies and are sold all over the world. This time, the trends of MPS as a drug development tool, especially case of safety assessment, issues for implementation to pharma, and future perspectives are described in this article.

Simplicity: Web-Based Visualization and Analysis of High-Throughput Cancer Cell Line Screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatics skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high- throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

Advancement of Network Pharmacology in Multi-targeted Therapeutic Evaluation of Medicinal Plants

In recent years, network pharmacology has emerged as a powerful approach for understanding the complex interactions between various bioactive components of medicinal plants and their targets within biological systems [1,2]. This approach plays a pivotal role in evaluating the multi-targeted therapeutic potential of medicinal plants, making it a promising tool for drug discovery and development. The integration of traditional knowledge with modern analytical techniques and computational methods has enabled the comprehensive exploration of the pharmacological properties of medicinal plants [3-6]. Medicinal plants have been an essential part of traditional medicine systems worldwide for centuries. These plants contain a wide array of bioactive compounds, such as alkaloids, flavonoids, terpenoids, and polyphenols, which exert therapeutic effects through interactions with specific molecular targets [7,8]. However, elucidating the intricate relationships between these compounds and their targets has proven challenging [2].