Data collection on research perspectives for drug development targeting vectorborne diseases and environmental impact
Ontology highlight
ABSTRACT:
ORGANISM(S): Parasitic Vector Borne Diseases
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PROVIDER: S-BSST1447 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Rare diseases affect over 400 million people worldwide and less than 5% of rare diseases have an approved treatment. Fortunately, the number of underlying disease etiologies is far less than the number of diseases, because many rare diseases share a common molecular etiology. Moreover, many of these shared molecular etiologies are therapeutically actionable. Grouping rare disease patients for clinical trials based on the underlying molecular etiology, rather than the traditional, symptom-based definition of disease, has the potential to greatly increase the number of patients gaining access to clinical trials. Basket clinical trials based on a shared molecular drug target have become common in the field of oncology and have been accepted by regulatory agencies as a basis for drug approvals. Implementation of basket clinical trials in the field of rare diseases is seen by multiple stakeholders-patients, researchers, clinicians, industry, regulators, and funders-as a solution to accelerate the identification of new therapies and address patient's unmet needs.
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Project description:Purpose of Review:Data science is an exploding trans-disciplinary field that aims to harness the power of data to gain information or insights on researcher-defined topics of interest. In this paper we review how data science can help advance environmental health research. Recent Findings:We discuss the concepts computationally scalable handling of Big Data and the design of efficient research data platforms, and how data science can provide solutions for methodological challenges in environmental health research, such as high-dimensional outcomes and exposures, and prediction models. Finally, we discuss tools for reproducible research. Summary:In this paper we present opportunities to improve environmental research capabilities by embracing data science, and the pitfalls that environmental health researchers should avoid when employing data scientific approaches. Throughout the paper, we emphasize the need for environmental health researchers to collaborate more closely with biostatisticians and data scientists to ensure robust and interpretable results.
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Project description:Antisense oligonucleotides (ASOs) are an increasingly represented class of drugs. These small sequences of nucleotides are designed to precisely target other oligonucleotides, usually RNA species, and are modified to protect them from degradation by nucleases. Their specificity is due to their sequence, so it is possible to target any RNA sequence that is already known. These molecules are very versatile and adaptable given that their sequence and chemistry can be custom manufactured. Based on the chemistry being used, their activity may significantly change and their effects on cell function and phenotypes can differ dramatically. While some will cause the target RNA to decay, others will only bind to the target and act as a steric blocker. Their incredible versatility is the key to manipulating several aspects of nucleic acid function as well as their process, and alter the transcriptome profile of a specific cell type or tissue. For example, they can be used to modify splicing or mask specific sites on a target. The entire design rather than just the sequence is essential to ensuring the specificity of the ASO to its target. Thus, it is vitally important to ensure that the complete process of drug design and testing is taken into account. ASOs' adaptability is a considerable advantage, and over the past decades has allowed multiple new drugs to be approved. This, in turn, has had a significant and positive impact on patient lives. Given current challenges presented by the COVID-19 pandemic, it is necessary to find new therapeutic strategies that would complement the vaccination efforts being used across the globe. ASOs may be a very powerful tool that can be used to target the virus RNA and provide a therapeutic paradigm. The proof of the efficacy of ASOs as an anti-viral agent is long-standing, yet no molecule currently has FDA approval. The emergence and widespread use of RNA vaccines during this health crisis might provide an ideal opportunity to develop the first anti-viral ASOs on the market. In this review, we describe the story of ASOs, the different characteristics of their chemistry, and how their characteristics translate into research and as a clinical tool.
| S-EPMC8781596 | biostudies-literature
Project description:BackgroundEpidemiologic cohort studies have begun to leverage electronic research participant portals to facilitate data collection, integrate wearable technologies, lower costs, and engage participants. However, little is known about the acceptability of portal use by research participants.ObjectiveThe aim of this study is to conduct focus groups among a sample of Cancer Prevention Study-3 (CPS-3) participants to better understand their preferences and concerns about research portals.MethodsCPS-3 participants were stratified based on sex, race and ethnicity, age, and cancer status, and randomly invited to participate. Focus groups used an exploratory case design with semistructured guides to prompt discussion. Using a constant comparison technique, transcripts were assigned codes to identify themes.ResultsParticipants (31/59, 52% women; 52/59, 88% White/non-Latinx) were favorably disposed toward using a research participant portal to take surveys, communicate with the study staff, and upload data. Most participants indicated that a portal would be beneficial and convenient but expressed concerns over data safety. Participants stressed the importance of an easy-to-use and trustworthy portal that is compatible with mobile devices.ConclusionsIn addition to being beneficial to researchers, portals may also benefit participants as long as the portals are secure and simple. Participants believe that portals can provide convenient ways to report data and remain connected to the study.
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Project description:Antibody-drug conjugates (ADCs) are targeted therapeutics generated by conjugation of cytotoxic small molecules to monoclonal antibodies (mAbs) via chemical linkers. Due to their selective delivery of toxic payloads to antigen-positive cancer cells, ADCs demonstrate wider therapeutic indexes compared with conventional chemotherapy. After decades of intensive research and development, significant advances have been made in the field, leading to a total of 10 U.S. food and drug administration (FDA)-approved ADCs to treat cancer patients. Currently, ~80 ADCs targeting different antigens are under clinical evaluation for treatment of either hematological or solid malignancies. Notably, three ADCs targeting the same oncofetal protein, receptor tyrosine kinase like orphan receptor 1 (ROR1), have attracted considerable attention when they were acquired or licensed successively in the fourth quarter of 2020 by three major pharmaceutical companies. Apparently, ROR1 has emerged as an attractive target for cancer therapy. Since all the components of ADCs, including the antibody, linker and payload, as well as the conjugation method, play critical roles in ADC's efficacy and performance, their choice and combination will determine how far they can be advanced. This review summarizes the design and development of current anti-ROR1 ADCs and highlights an emerging trend to target ROR1 for cancer therapy.
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Project description:BackgroundIn 2007, the priority review voucher (PRV) was implemented in the US to incentivize research and development (R&D) for tropical diseases. The PRV is issued by the US FDA and grants a quicker review to manufacturers upon successful development of a product for a disease eligible for the program.ObjectiveThe objective of this analysis was to assess whether the PRV has incentivized R&D (measured as clinical trial activity) for the intended tropical diseases.MethodWe used a difference-in-difference-in-differences (DDD) strategy by exploiting variation in its implementation across diseases and registries around the world. Clinical trials were retrieved from the World Health Organization International Clinical Trials Registry Platform for the years 2005-2019.ResultsWe found a positive, but not statistically significant, effect of the PRV on stimulating R&D activity. Delayed effects of the policy could not be found.ConclusionOur findings, which were robust across a series of robustness tests, suggest that the PRV program is not associated with a trigger in innovation for neglected diseases and therefore should not be considered as a stand-alone solution. It should be supplemented with other government measures to incentivize R&D activity. To increase the value of the program, we recommend that the PRV only be awarded to novel products and not to products that have already been licensed outside the US. Doing so would restrict the number of vouchers awarded and slow down their ongoing market depreciation. Finally, we propose that product sponsors be required to submit an access plan for PRV-awarded products.
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Project description:Online data collection methods are expanding the ease and access of developmental research for researchers and participants alike. While its popularity among developmental scientists has soared during the COVID-19 pandemic, its potential goes beyond just a means for safe, socially distanced data collection. In particular, advances in video conferencing software has enabled researchers to engage in face-to-face interactions with participants from nearly any location at any time. Due to the novelty of these methods, however, many researchers still remain uncertain about the differences in available approaches as well as the validity of online methods more broadly. In this article, we aim to address both issues with a focus on moderated (synchronous) data collected using video-conferencing software (e.g., Zoom). First, we review existing approaches for designing and executing moderated online studies with young children. We also present concrete examples of studies that implemented choice and verbal measures (Studies 1 and 2) and looking time (Studies 3 and 4) across both in-person and online moderated data collection methods. Direct comparison of the two methods within each study as well as a meta-analysis of all studies suggest that the results from the two methods are comparable, providing empirical support for the validity of moderated online data collection. Finally, we discuss current limitations of online data collection and possible solutions, as well as its potential to increase the accessibility, diversity, and replicability of developmental science.
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Project description:Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug development and the pharmaceutical industry and regulatory authorities. Implementation of predictive systems in the clinics is more than ever a reality, resulting in a surge of interest for PBPK models by clinicians. We aimed to establish a repository of available PBPK models developed to date to predict drug-drug interactions (DDIs) in the different therapeutic areas by integrating intrinsic and extrinsic factors such as genetic polymorphisms of the cytochromes or environmental clues. This work includes peer-reviewed publications and models developed in the literature from October 2017 to January 2021. Information about the software, type of model, size, and population model was extracted for each article. In general, modeling was mainly done for DDI prediction via Simcyp® software and Full PBPK. Overall, the necessary physiological and physio-pathological parameters, such as weight, BMI, liver or kidney function, relative to the drug absorption, distribution, metabolism, and elimination and to the population studied for model construction was publicly available. Of the 46 articles, 32 sensibly predicted DDI potentials, but only 23% integrated the genetic aspect to the developed models. Marked differences in concentration time profiles and maximum plasma concentration could be explained by the significant precision of the input parameters such as Tissue: plasma partition coefficients, protein abundance, or Ki values. In conclusion, the models show a good correlation between the predicted and observed plasma concentration values. These correlations are all the more pronounced as the model is rich in data representative of the population and the molecule in question. PBPK for DDI prediction is a promising approach in clinical, and harmonization of clearance prediction may be helped by a consensus on selecting the best data to use for PBPK model development.
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