scholarly journals Using Genetically Modified Rodent Models in Drug Development to Explore Target Physiology and Potential Drug Effects

2018 ◽  
Vol 55 (2) ◽  
pp. 193-194
Author(s):  
Yu Zhong ◽  
Donna M. Dambach ◽  
Jonathan M. Maher
2018 ◽  
Vol 243 (14) ◽  
pp. 1125-1132 ◽  
Author(s):  
Jennifer L Wilson

An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging. Impact statement Many untreated diseases are not monogenic and are instead caused by multiple genetic defects. Because of this complexity, computational, logical, and systems understanding will be essential to discovering novel therapies. The scientist engineer is uniquely disposed to use this type of understanding to advance therapeutic discovery. This work highlights benefits of the scientist engineer perspective and underscores the potential impact of these approaches for future therapeutic development. By framing the scientist engineer’s tool set and increasing awareness about this approach, this article stands to impact future therapeutic development efforts in an age of rising development costs and high drug attrition.


2016 ◽  
Vol 95 (8) ◽  
pp. 1540-1547 ◽  
Author(s):  
Yiting Cui ◽  
Su Yang ◽  
Xiao-Jiang Li ◽  
Shihua Li

2021 ◽  
Vol 12 ◽  
Author(s):  
Michael J. Iadarola ◽  
Dorothy Cimino Brown ◽  
Alexis Nahama ◽  
Matthew R. Sapio ◽  
Andrew J. Mannes

One of the biggest challenges for analgesic drug development is how to decide if a potential analgesic candidate will work in humans. What preclinical data are the most convincing, incentivizing and most predictive of success? Such a predicament is not unique to analgesics, and the pain field has certain advantages over drug development efforts in areas like neuropsychiatry where the etiological origins are either unknown or difficult to ascertain. For pain, the origin of the problem frequently is known, and the causative peripheral tissue insult might be observable. The main conundrum centers around evaluation of translational cell- and rodent-based results. While cell and rodent models are undeniably important first steps for screening, probing mechanism of action, and understanding factors of adsorption, distribution metabolism and excretion, two questions arise from such studies. First, are they reliable indicators of analgesic performance of a candidate drug in human acute and chronic pain? Second, what additional model systems might be capable of increasing translational confidence? We address this second question by assessing, primarily, the companion canine model, which can provide particularly strong predictive information for candidate analgesic agents in humans. This statement is mainly derived from our studies with resiniferatoxin (RTX) a potent TRPV1 agonist but also from protein therapeutics using a conjugate of Substance P and saporin. Our experience, to date, is that rodent models might be very well suited for acute pain translation, but companion canine models, and other large animal studies, can augment initial discovery research using rodent models for neuropathic or chronic pain. The larger animal models also provide strong translational predictive capacity for analgesic performance in humans, better predict dosing parameters for human trials and provide insight into behavior changes (bladder, bowel, mood, etc.) that are not readily assessed in laboratory animals. They are, however, not without problems that can be encountered with any experimental drug treatment or clinical trial. It also is important to recognize that pain treatment is a major veterinary concern and is an intrinsically worthwhile endeavor for animals as well as humans.


2020 ◽  
Author(s):  
Nima Rajabi ◽  
Alexander Lund Nielsen ◽  
Christian Adam Olsen

The sirtuin enzymes are potential drug targets for intervention in a series of diseases. Efforts to inhibit enzymes of this class with thioamide- and thiourea-containing, substrate-mimicking entities have produced a number of high-affinity binders. However, less attention has been dedicated to the investigation of the stability of these inhibitors under various conditions. Here, we provide evidence of unprecedented degree of cleavage of short-chain epsilon-<i>N</i>-thioacyllysine modifications meant to target these sirtuins and further provide insights into the serum stability of compounds containing both thioamides and thioureas. Our study questions the utility short-chain thioamide-based inhibitors of sirtuins for drug development and points to application of mono-alkylated thiourea-based chemotypes as more promising for targeting sirtuins 1 and 3, in particular.


2018 ◽  
Vol 18 (20) ◽  
pp. 1745-1754 ◽  
Author(s):  
Sneha Rai ◽  
Utkarsh Raj ◽  
Pritish Kumar Varadwaj

The conventional way of characterizing a disease consists of correlating clinical symptoms with pathological findings. Although this approach for many years has assisted clinicians in establishing syndromic patterns for pathophenotypes, it has major limitations as it does not consider preclinical disease states and is unable to individualize medicine. Moreover, the complexity of disease biology is the major challenge in the development of effective and safe medicines. Therefore, the process of drug development must consider biological responses in both pathological and physiological conditions. Consequently, a quantitative and holistic systems biology approach could aid in understanding complex biological systems by providing an exceptional platform to integrate diverse data types with molecular as well as pathway information, leading to development of predictive models for complex diseases. Furthermore, an increase in knowledgebase of proteins, genes, metabolites from high-throughput experimental data accelerates hypothesis generation and testing in disease models. The systems biology approach also assists in predicting drug effects, repurposing of existing drugs, identifying new targets, facilitating development of personalized medicine and improving decision making and success rate of new drugs in clinical trials.


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