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Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
It is now recognized that the desired effects of most therapeutics are exerted via modulation of multiple targets and pathways [164–167], whereas severe side effects can sometimes be associated with the excessive selectivity of a drug for a single target [168]. Moreover, it deals with pharmaceutical agents characterized by the promiscuous binding to multiple targets and acting on multiple disease pathways, thereby generating different phenotypic or pharmacological effects [167]. Polypharmacology deals with multi-target binding, drug off targeting, and molecular promiscuity, and thereby opposes the “single drug, single target” approach. Therefore, polypharmacology has emerged as a powerful alternative paradigm for development of versatile therapeutic agents capable of modulating multiple biological targets simultaneously, often displaying higher efficacy, less resistance, and an improved safety profile [169]. It is important to emphasize that, while in the past the identification of multi-targeting agents has largely been fortuitous and serendipitous, recent advances in computational sciences enable rational design of drug polypharmacology (reviewed in [167, 170, 171]). Since a single therapeutic can act on multiple targets and a single target can be affected by multiple therapeutics, one can differentiate ligand-based and target-based polypharmacology, whereas network pharmacology integrates multi-omics technologies and systems biology for drug discovery and development [172].
Machines with Radionuclide Sources
Published in W. P. M. Mayles, A. E. Nahum, J.-C. Rosenwald, Handbook of Radiotherapy Physics, 2021
John Saunders, Lee Walton, Katharine Hunt
The PCI can be split into two separate indices: coverage (C) and selectivity (S). These separated indices can be more helpful when optimising a plan depending on the clinical requirements. Coverage represents the amount of the target, which is covered by the prescription isodose. Perfect coverage is indicated by a score of 1, and coverage <1 represents under-treatment of the target. Selectivity represents a measure of the amount of normal tissue receiving doses greater than the prescription isodose. Perfect selectivity is indicated by a score of 1, and selectivity <1 represents over-treatment of normal tissue.
The Evolution of Anticancer Therapies
Published in David E. Thurston, Ilona Pysz, Chemistry and Pharmacology of Anticancer Drugs, 2021
A further problem is that most drug targets, ranging from nucleic acids to proteins, are typically present in cancer and healthy cells in similar proportions, so it is difficult to achieve the selectivity necessary for effective treatment. This contrasts with bacterial, viral, and fungal infections where there are unique drug targets in the invading organisms such as cell wall synthesis in bacteria (i.e., targeted by the penicillins), thus allowing highly effective anti-infective agents to be developed that are virtually devoid of toxicity toward healthy host cells. The relative paucity of clearly identifiable biochemical differences between healthy and tumor cells has delayed the discovery and development of effective anticancer agents devoid of toxicity.
Ribosomopathies and cancer: pharmacological implications
Published in Expert Review of Clinical Pharmacology, 2022
Gazmend Temaj, Sarmistha Saha, Shpend Dragusha, Valon Ejupi, Brigitta Buttari, Elisabetta Profumo, Lule Beqa, Luciano Saso
However, there are still some challenges associated with these inhibitors, such as toxicity evaluation, selectivity, bioavailability, and pharmacokinetic properties. Therefore, further in vitro and in vivo studies using different cancer models are needed. Moreover, well-defined large randomized clinical trials could identify therapeutic molecules, as well as their concise effects on cancer biology and in a wide range of multiple disease models. Moreover, since available evidence from prospective trials is limited, we believe that a synergistic combination of therapeutic compounds targeting different approaches to ribosome biogenesis will likely yield many more therapeutic strategies in the future. Thus, mechanistic research on the rewiring of ribosome biogenesis would provide a stimulus for researchers to design and develop novel therapeutic approaches aimed at the selective regulation of translational circuits in ribosome biogenesis.
How can we improve peptide drug discovery? Learning from the past
Published in Expert Opinion on Drug Discovery, 2021
With more than 80 peptides now FDA approved and hundreds more in preclinical or clinical trials there is no doubt that peptides are having an impact in the pharmaceutical industry [1–4]. That this is occurring reflects the undoubted advantages of peptides, including high potency and selectivity for their therapeutic targets relative to traditional small molecule therapeutics. The potency and selectivity in part derives from the larger size of peptides relative to small molecule drugs, allowing them to capture more binding interactions with target receptors, thus having high affinity (and hence potency) for those receptors but not with others (i.e. driving selectivity). Additionally, peptides have the advantage of filling a gap in molecular size space between small molecule therapeutics (<500 Da) and larger (>5000 Da) protein-based biologics [3]. Biologics generally do have excellent specificity and potency but are typically not orally available like their small molecule counterparts and are generally more expensive than small molecule drugs. Peptides thus have the potency advantage of biologics and the cost advantage of small molecule drugs. Furthermore, peptides have the potential to address classes of targets that previously have been regarded as ‘undruggable,’ including protein–protein interactions in the intracellular space [5].
Tetracaine from urethral ointment causes false positive amphetamine results by immunoassay
Published in Clinical Toxicology, 2021
Robin Wijngaard, Marina Parra-Robert, Lourdes Marés, Anna Escalante, Emilio Salgado, Bernardino González-de-la-Presa, Jordi To-Figueras, Mercè Brunet
Unfortunately, drug screening results need to be interpreted carefully due to several limitations associated with the IA methods [1–4]. Firstly, specific cut-off values are established for each drug and only results above these cut-offs are expressed as positive. Therefore, negative urine screening results do not necessarily indicate the absence of a certain drug but rather a concentration below the cut-off value [1,4]. Secondly, IAs can cross-react with structurally-related compounds other than the primary antibody target [1,5]. This lack of selectivity can be advantageous as it is possible to detect a broader range of compounds of the same drug class which may have the same clinical relevance. However, cross-reactivity with other structure-related substances can result in a decrease in diagnostic specificity. Consequently, a positive result does not assure the presence of the targeted drug or metabolite in the urine sample [1,4,5]. Commonly prescribed medications have been found to cause false positive (FP) results possibly leading to misinterpretation of results and consequently to inappropriate clinical decisions. For example, sertraline has been reported to cause FP benzodiazepine results whereas quinolones (such as levofloxacin or ofloxacin) are known to interfere with the opiate IA. [4,6–8].