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Phytonanotechnology
Published in Namrita Lall, Medicinal Plants for Cosmetics, Health and Diseases, 2022
Tafadzwa J. Chiome, Asha Srinivasan
Once a target is identified, it is then validated for a response, which is critical in the disease process. The next step in the drug discovery process is identification, which involves discovery of the compound that will bind and interact with the target. A library of compounds is screened against the chosen validated target, and suitable hits are grown and developed into larger lead-like compounds. Several screening techniques such as high throughput screening, fragment-based screening and/or virtual screening can be used (Baker et al., 1995). Compounds in the hit series are optimized to improve potency and selectivity, which is achieved through functional group modifications. The identified lead compounds are modified to further improve the physicochemical and biological properties, making the compounds more effective and safer while transforming them into more viable drug candidates. The optimized compounds are then taken through the drug development process, starting with preclinical trials which lead to clinical testing (Baker et al., 1995).
Long-Term Toxicity and Regulations for Bioactive-Loaded Nanomedicines
Published in Mahfoozur Rahman, Sarwar Beg, Mazin A. Zamzami, Hani Choudhry, Aftab Ahmad, Khalid S. Alharbi, Biomarkers as Targeted Herbal Drug Discovery, 2022
Iqbal Ahmad, Sobiya Zafar, Shakeeb Ahmad, Suma Saad, S. M. Kawish, Sanjay Agarwal, Farhan Jalees Ahmad
High throughput screening (HTS) and high content screening (HCS) are the automated tools used for the estimation of in vitro toxicity related to NMs. HTS is mainly a type of automated assay and usually focuses on a separate biological mechanism or biochemical alteration. HTS-based screening of test NMs do not observe the whole phenotypical changes in cell. HCS is also an automated screening tool and mainly uses fluorescence and microscopic images for the toxicity analysis. Contrary to HTS, it is used to estimate multiple changes in the phenotype of similar cell population (Godwin et al., 2015) The HTS based in vitro assays are comparatively simple, less time consuming and less expensive than complicated animal model experiments. However, it cannot be used solely for the in vitro toxicity study due to the poor in vitro-in vivo correlation (IVIC); as a small cell cannot be a representative of an animal with complex body structure.
Evaluation Models for Drug Transport Across the Blood–Brain Barrier
Published in Sahab Uddin, Rashid Mamunur, Advances in Neuropharmacology, 2020
In vitro models find a suitable role in new drug research and development process which includes various stages like target identification, hit identification, lead identification, and finally optimization of the product. The first stage involves screening numerous compounds by high throughput screening when a target is identified. Simple models like monolayer and co-culture models are mainly used in the first step. The validation of identified compound and SAR are usually carried out in optimization stage which utilizes in vitro models like static co-culture and dynamic models that are sensitive to in vivo conditions. The correlation with human cells is required to be carried out to avoid interspecies variability at various stages of development (Paradis et al., 2016).
3D bioprinting for organ and organoid models and disease modeling
Published in Expert Opinion on Drug Discovery, 2023
Amanda C. Juraski, Sonali Sharma, Sydney Sparanese, Victor A. da Silva, Julie Wong, Zachary Laksman, Ryan Flannigan, Leili Rohani, Stephanie M. Willerth
A recent review on high-throughput screening assays highlighted three crucial principles for selecting targets for high-throughput screening: disease relevance, chemical tractability, and screenability [62]. Disease relevance means selecting targets that have strong links to clinical disease. However, sometimes the most valid targets may not be novel, while highly novel targets may not have a strong correlation to disease. Chemical tractability refers to the probability of finding a compound that produces the desired effect and is compatible with the screening technology. Finally, screenability is the ability to develop a high-quality screening assay that is robust and effective. These three principles provide a framework for future screening assays that use three-dimensional organoids and cell assays. With the increasing use of 3D bioprinting in drug discovery, it is crucial to select the most appropriate targets for screening. The use of 3D bioprinting offers a physiologically relevant environment that can improve drug screening accuracy. The incorporation of relevant cell types and the ability to mimic in vivo-like environments provides a valuable tool to understand drug responses and improve drug discovery.
Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform
Published in Expert Opinion on Drug Discovery, 2022
Mickaël Guedj, Jack Swindle, Antoine Hamon, Sandra Hubert, Emiko Desvaux, Jessica Laplume, Laura Xuereb, Céline Lefebvre, Yannick Haudry, Christine Gabarroca, Audrey Aussy, Laurence Laigle, Isabelle Dupin-Roger, Philippe Moingeon
Researchers can then validate target hypotheses, through an experimental confirmation that disease activity is impacted following perturbation of the target of interest with a drug or a tool compound. Conducting wet-lab gene inhibition (e.g. via CRISPR-Cas9 deletion or RNA silencing) or preclinical experiments by using cellular assays or animal models are commonly implemented to corroborate the hypothesis that drugs interacting with the target exhibit the anticipated pharmacological activity. Once a therapeutic target has been selected, multiple processes streamlined by the pharmaceutical industry can be used to identify small molecules or biologicals interacting with it. For instance, High-Throughput Screening (HTS) can be implemented to test the company’s proprietary compound library in various molecular or cell-based assay systems in order to identify drug candidates [65]. As of today, another strategy relies upon dedicated computational methods to select in silico drugs predicted to engage the target of interest [15].
Transforming cancer drug discovery with Big Data and AI
Published in Expert Opinion on Drug Discovery, 2019
Paul Workman, Albert A. Antolin, Bissan Al-Lazikani
Over the past 25 years, drug discovery has benefited from the implementation of many innovative approaches and technologies. Next generation sequencing, together with large-scale RNAi interference and more recently CRISPR technology, have proved important for mechanistic biological exploration and identification of drug targets, especially in oncology. We have seen growth in the size and diversity of chemical libraries. High-throughput screening, with assay refinements that utilize robotics and microfluidics, have increased the availability of chemical matter acting on drug targets, while structure- and fragment-based design have enhanced hit-to lead and optimization of small molecules. And recombinant DNA technology has revolutionized the design of therapeutic antibodies and other biologicals.