Methods for Elucidation of DNA-Anticancer Drug Interactions and their Applications in the Development of New Drugs

2017 ◽  
Vol 22 (44) ◽  
pp. 6596-6611 ◽  
Author(s):  
Majus Misiak ◽  
Francesco Mantegazza ◽  
Giovanni L. Beretta
Keyword(s):  
Drug Interactions ◽  
Anticancer Drug ◽  
New Drugs

scholarly journals TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs

2018 ◽  
Vol 19 (S14) ◽  
Author(s):  
Jian-Yu Shi ◽  
Hua Huang ◽  
Jia-Xin Li ◽  
Peng Lei ◽  
Yan-Ning Zhang ◽  
...  
Keyword(s):  
Drug Interactions ◽  
Matrix Factorization ◽  
New Drugs ◽  
Unified Framework

scholarly journals Drug interaction and rational drug therapy in hospitalized patients reported with stroke

2020 ◽  
Vol 1 (1) ◽  
pp. 15-22
Author(s):  
Fawad Ali Shah ◽  
Muhammad Imran Khan ◽  
Bashir Ahmad ◽  
Shabir Hussain
Keyword(s):  
Drug Interactions ◽  
Past History ◽  
Disease Incidence ◽  
Drug Prescription ◽  
Demographic Data ◽  
Relevant Information ◽  
Hospitalized Patients ◽  
New Drugs ◽  
Biochemical Tests ◽  
Patient Demographic Data

Objective: The purpose of the current study was to determine prescription rationalities, any pharmacotherapy-based problems, and to determine drug interactions in patients with stroke. Methods: Patient case histories were determined using a standard questionnaire form having a patient tag, age, sex, past history, chief complaints, biochemical tests, treatment chart and other relevant information. Forty patients suffering from stroke and were on treatment were selected for the current study. The relevant information was recorded with respect to patient demographic data, disease incidence, drug interactions.  Results: Most of the hospitalized patients were in the range of 51-60 (20% out of 40 patients) and 71-80 (22.5% out of 40 patients) years. The most frequent cause of hospitalization was Cerebro Vascular Accident (CVA) or stroke (57.5%). New drugs were added to the regimen of 37.5% of patients due to certain diseases. The dose was changed in the regimen of 17.5% of patients. In 7.5% patient dosage form was changed. 27.5% of patients were observed with the therapeutic alternative. Drug interactions were found in the prescription history of 30% of patients. Conclusion: Hence, it is concluded that most of the patients admitted to hospital with Cerebro Vascular Accident and stroke have irrational drug prescription and drug-drug interactions in their prescription history.

The next generation of machine learning in DDIs prediction

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Huang ◽  
Chunyan Li ◽  
Ying Ju ◽  
Yan Gao
Keyword(s):  
Drug Interactions ◽  
Adverse Reactions ◽  
Computational Method ◽  
New Drugs ◽  
Computational Techniques ◽  
Data Sets ◽  
Review Of The Literature ◽  
Pharmacokinetics And Pharmacodynamics ◽  
Challenges And Opportunities ◽  
Drug Assays

: Drug-drug interactions may occur when to combine two or more drugs and may cause some adverse events such as Cardiotoxicity, Central neurotoxicity, Hepatotoxicity, etc. Although a large number of researchers who are proficient in pharmacokinetics and pharmacodynamics have been engaged in drug assays and trying to find out the side effects of all kinds of drug combinations. However, at the same time, the number of new drugs is increasing dramatically, and the drug assay is an expensive and time-consuming process. It is impossible to find all the adverse reactions through drug experiments. Therefore, new attempts have risen in using computational techniques to deal with this problem. In this review, we conduct a review of the literature on applying the computational method for predicting drug-drug interactions. We first briefly introduce the widely used data sets. After that, we elaborate on the existing state-of-art deep learning models for drug-drug interactions prediction. We also discussed the challenges and opportunities of applying the computational method in drug-drug interactions prediction.

scholarly journals Personalizing protein-drug interactions

2008 ◽  
Vol 80 (8) ◽  
pp. 1811-1820
Author(s):  
Natasha Beeton-Kempen ◽  
Aubrey Shoko ◽  
Jonathan Blackburn
Keyword(s):  
Drug Interactions ◽  
Prescription Drugs ◽  
Protein Function ◽  
Great Majority ◽  
New Drugs ◽  
Protein Drug ◽  
Specific Patient ◽  
Approved Drugs ◽  
The Individual ◽  
The Right

The development of new drugs today is a hugely expensive process, with estimated costs of up to $1 billion to take a drug through to market. However, despite this seemingly massive expenditure, statistics show that the great majority of prescription drugs on the market today are only effective for around 40 % of the patients to whom they are administered. Worse still, recently there have been a series of high-profile instances where potentially block-busting FDA-approved drugs have subsequently been withdrawn due to unanticipated side effects that were only revealed when the drug entered use in the general population. A variety of factors are at play in underpinning such statistics, but at the heart of the problem is the fact that, despite the extensive knowledge being generated in the postgenomic era about the genetic differences between individuals, Western medicine still today largely ignores such differences. The hope therefore is that by gaining a greater understanding of the individual nature of disease progression and of drug response, we might move toward a new era of personalized medicine in which the right drug is prescribed at the right dose to treat the precise disease afflicting the specific patient. As a step along this road, this review will discuss new approaches in the pharmacogenomics field to understanding in a quantitative manner the molecular consequence of polymorphic variation and mutation, both on encoded protein function and on protein-drug interactions.

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