ABSTRACT
Introduction Cancer chemotherapy regimens include multiple classes of adjuvant drugs as supportive therapy. Because of the concurrent intake of other drugs (like antiemetics, antidepressants, analgesics, and antimicrobials), there is a heightened risk for possible QT interval prolongation. There is a dearth of evidence in the literature regarding the usage of QT-prolonging anticancer drugs and associated risk factors that have the propensity to prolong QT interval. The purpose was to explore the extent of the use of QT-interval-prolonging drugs and potential QT-prolonging drug-drug interactions (QT-DDIs) in cancer patients attending OPD in a tertiary-care hospital. Methods This was a hospital-based, cross-sectional, observational study. Risk stratification of QT-prolonging drugs for torsades de pointes (TdP) was done by the Arizona Center for Education and Research on Therapeutics (AzCERT)/CredibleMeds-lists, and potential QT-DDIs were determined with four online DDI-checker-software. Results In 1331 cancer patients, the overall prevalence of potential QT-prolonging drug utilization was 97.3%. Ondansetron, pantoprazole, domperidone, and olanzapine were the most frequent QT-prolonging drugs in cancer patients. The top six antineoplastics with potential QT-prolonging and torsadogenic actions were capecitabine, oxaliplatin, imatinib, bortezomib, 5-fluorouracil, and bendamustine. Evidence-based pragmatic QTc interval prolongation risk assessment tools are imperative for cancer patients. Conclusion This study revealed a high prevalence of QT-prolonging drugs and QT-DDIs among cancer patients who are treated with anticancer and non-anticancer drugs. As a result, it's critical to take precautions, stay vigilant, and avoid QT-prolonging in clinical situations. Evidence-based pragmatic QTc interval prolongation risk assessment tools are needed for cancer patients.
ABSTRACT
The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.
