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The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
Finally, through the years, all of the above approaches have given way to modern methods of drug discovery based on the application of structural biology technologies to identify and obtain the structure of biologically relevant target proteins or receptors, followed by the identification of small molecules to accurately interact with them using techniques such as rationale high-throughput screening and/or in silico methods. This top-level approach, which is now considered to be the “cutting edge” of drug discovery, delivered the first Precision Medicines imatinib (GlivecTM) and trastuzumab (HerceptinTM), and is the pathway that most drug discovery companies and academic groups are now following (Figure 2.3). Diagram showing the process of structure-based drug discovery which starts with the structural determination of the drug target (usually a protein or receptor) using X-Ray crystallography or high-field NMR followed by physical or virtual screening to identify a lead molecule.
Paediatric clinical pharmacology
Published in Evelyne Jacqz-Aigrain, Imti Choonara, Paediatric Clinical Pharmacology, 2021
Evelyne Jacqz-Aigrain, Imti Choonara
Target proteins can be either a receptor, an enzyme, or another type of protein. Genetic polymorphisms affecting these drug targets can contribute to the pathogenesis of the disease and modify the response to specific medications in children. As an example, polymorphisms of the β2-adrenergic receptor (ADRB2) have been implicated in the response to β2-agonists in patients with asthma [47]. Polymorphisms in the promotor region (variable number of tandem repeats) affecting ALOX5 gene expression have been associated with the response to inhibitors of ALOX5 [48]. A number of additional polymorphisms of potential clinical importance were described in adults (for review see [49]) (Table 3).
A Brief History of Genetic Therapy: Gene Therapy, Antisense Technology, and Genomics
Published in Eric Wickstrom, Clinical Trials of Genetic Therapy with Antisense DNA and DNA Vectors, 2020
Antisense technologies are related by the fact that the active biomolecule for each is a polymeric nucleic acid. Except in the case of some aptamer applications, each of these technologies also focuses on a specific hybridization interaction with another unique nucleic acid target to achieve activity and selectivity in biological action. Sequence specificity is also at the heart of the therapeutic promise of antisense compounds; antisense addresses itself to the genotype of an organism and can target a specific gene or gene transcription product to achieve selective therapeutic effects not possible through drugs which target protein phenotype.
Recent advances in IAP-based PROTACs (SNIPERs) as potential therapeutic agents
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2022
Chao Wang, Yujing Zhang, Lingyu Shi, Shanbo Yang, Jing Chang, Yingjie Zhong, Qian Li, Dongming Xing
Traditional small-molecule drugs have dramatically changed the face of diseases treatment in the last few decades. A variety of inhibitors, agonists, and antagonists have entered clinical use1–3. They are characterised by more acceptable pharmacokinetic properties and desirable oral bioavailabilities, as well as lower manufacturing costs. However, traditional small-molecule drugs still have many weaknesses that limit their application on non-pharmacological proteins such as transcription factors, scaffolding proteins, and non-enzymatic proteins. In terms of mode of action, traditional small-molecule drugs exert their effects by occupying active pocket sites, which requires high doses of administration to maintain activities. This increases the risk of off-targeting and leads to adverse effects. Moreover, refractory drug targets do not bind effectively to traditional small-molecule drugs. Furthermore, genetic mutations often lead to changes in protein conformation that can result in resistance to traditional small-molecule drugs. In addition, sustained inhibition of the target proteins may lead to compensatory overexpression of the proteins, which may greatly increase the risk of acquired drug resistances4–6. Therefore, it is of great importance to develop new technologies to address these issues.
Identification of novel mycocompounds as inhibitors of PI3K/AKT/mTOR pathway against RCC
Published in Journal of Receptors and Signal Transduction, 2022
Ravi Prakash Yadav, Srilagna Chatterjee, Arindam Chatterjee, Dilip Kumar Pal, Sudakshina Ghosh, Krishnendu Acharya, Madhusudan Das
Virtual screening method was employed to evaluate high affinity mycocompounds from A. hygrometricus. The screening technique successfully reproduced the binding pattern of co-existing ligands in PI3K, AKT, and mTOR. The top ranked categorized mycocompounds displaying the free energy of binding in the range −4.7 to −10.0 kcal/mol are presented in Table 1. Among all the mycocompounds astrakurkurone and ergosta-4, 6, 8-(14) 22-tetraene-3-one showed highest interactions with the target proteins. The binding energies for astrakurkurone were −8.4, −10 and −8.2 kcal/mol for PI3K, AKT and mTOR proteins whereas for ergosta-4,6, 8-(14) 22-tetraene-3-one it was −8.8, −9.1 and −7.2 kcal/mol. for the target proteins respectively. Majority of the interactions between astrakurkurone and ergosta-4, 6, 8-(14) 22-tetraene-3-one with targets proteins were in accordance with previous studies [31].
Artificial intelligence in early drug discovery enabling precision medicine
Published in Expert Opinion on Drug Discovery, 2021
Fabio Boniolo, Emilio Dorigatti, Alexander J. Ohnmacht, Dieter Saur, Benjamin Schubert, Michael P. Menden
In summary, small molecule design has greatly benefited from AI’s capabilities of learning latent representations that drive the functional properties of such molecules and exploiting the euclidean structure of the resulting embedding manifold to improve said properties. This enabled efficient library design for high-throughput drug screenings which can ultimately translate into a significant increase in the success rate of downstream clinical trials, as poor drug candidates could be reliably identified and discarded in silico. Most generative models are trained end-to-end and only learn about physical plausibility implicitly, thus the produced molecules necessitate post-hoc structural fine-tuning using molecular dynamics. Similar to generative models for proteins, more physics-informed networks that constrain the latent space to regions yielding physicochemical viable solutions might improve deep learning-based de novo design. Even though the application of such models in a personalized setting has yet to be shown, we envision that with improved pharmacogenomics screening datasets, models will be soon developed that can generate novel molecules conditioned on mutational changes of their target protein.