China
Africa
Explore chapters and articles related to this topic
AI/ML in Medical Research and Drug Development
Published in Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow, Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
High-throughput Screening (HTS) is an experimentation method that is especially used in drug discovery. Active compounds, antibodies or genes that modulate certain pathways can be rapidly identified by this method. It has been employed in small-molecule drug discovery for the past two decades [Schneider et al., 2020]. With big amount of data from HTS and combinatorial synthesis, ML methods have shown to be an eminent tool in mining chemical information from large databases to design drugs with specific properties [Lo et al., 2018]. Selecting the most appropriate HTS hits is critical to drug discovery phase [Holenz and Stoy, 2019], which would highly impact the success of clinical trials. There are multiple criteria that must be considered in the hit selection process such as potency, toxicity, permeability, solubility, selectivity at desired pharmaceutical targets and physicochemical that impact pharmacokinetics and drug safety. Some of these criteria emerge from Absorption, Distribution, Metabolism, Excretion, and Toxicology (ADMET) properties of the compound. This very well fits into a challenging Multi-Objective Optimization (MOO) problem also known as multi-attribute optimization or Pareto optimization [Lambrinidis and Tsantili-Kakoulidou, 2018]. MOO involves finding a molecule that balances between different properties that are usually conflicting. The goal is then to find a lead among all possible ones that provides the best trade-off depending on the goal and application. [Perron et al., 2018] describes the use of deep learning to de novo design and address the MOO problem using ligand-based design methods.
High throughput and targeted screens for prepilin peptidase inhibitors do not identify common inhibitors of eukaryotic gamma-secretase
Published in Expert Opinion on Drug Discovery, 2023
Pradip Kumar Singh, Michael S. Donnenberg
All screening plates in which the Z’ factor was >0.5 or the SSMD was > +3 were considered for hit selections. During the initial screening of the Discovery Probe™ library, all plates for which the Z’ factor was >0.5 were considered for hit selection. However, for all subsequent screens, an SSMD value greater than +3 was set as the selection criterion, due to the superiority of SSMD over Z’ factor when effect sizes (positive control – negative control) vary [63,64]. We observed such variations, which we attributed to differences in end-point OD. All plates that failed the quality control criteria were repeated. For plates that passed quality control, compounds from wells that had FP values greater than or equal to the mean plus 3 sd of the plate’s negative control wells were selected as target compounds for the next step.
Challenges with risk mitigation in academic drug discovery: finding the best solution
Published in Expert Opinion on Drug Discovery, 2019
Investment of limited academic dollars into cost-intensive early discovery screens is a major issue encountered in academic settings. In many instances, gaps exist in knowledge and understanding of what comprises an innovative target for drug discovery, what defines target validation, and makes a target druggable [15]. Thus, finding a chemical scaffold or a peptidomimetic that binds in silico to a predicted protein model alone does not validate the target unless other experiments are performed to show functional modulation in a disease model. The complexity, cross-talks, multiple interactions, and protein complexes in biological systems may sometimes preclude precise and selective definition for target functionality. While phenotypic screens with no prior knowledge of target have been reported to result in several first-in-class drugs, it is essential that both the investigator, who has expansive knowledge of his biology, and the early discovery experts with knowledge of compound attrition workflows contribute collaboratively toward developing secondary and selectivity assays that will help fine-tune hit selection process. This collaboration can help define the questions that need to be explored from drug discovery perspective and the best possible way to unambiguously arrive at solutions for moving the project forward.
Current insights into anti-HIV drug discovery and development: a review of recent patent literature (2014–2017)
Published in Expert Opinion on Therapeutic Patents, 2018
Xiaofang Zuo, Zhipeng Huo, Dongwei Kang, Gaochan Wu, Zhongxia Zhou, Xinyong Liu, Peng Zhan
As we known, hit selection and lead generation are determining steps for the resource-intensive lead-optimization in drug discovery and are considered to be a major research area of current medicinal chemistry. High-throughput screening (HTS) of compound library from biochemical and cell based assay screens is still a useful and effective tool to identity new anti-HIV compounds. However, the hit rate of HTS is relatively low. Selection of compounds with the best developmental potential is key to the success of the more resource-demanding lead-optimization phase. An important question arises in how to design economically viable anti-HIV drugs based on known ligands or ligand–protein complex structures while keeping or improving potency and pharmacokinetic (PK) properties of existing drugs by seeking novel structural skeletons [3,8–11].