Systematic Review of Comparative Pharmacokinetic Profile of Drugs in Non-Communicable Chronic Diseases
Bibliographic record
Abstract
Introduction: Non-communicable chronic diseases (NCDs) such as diabetes, cardiovascular diseases, chronic kidney disease, and liver disorders alter drug pharmacokinetics, impacting absorption, distribution, metabolism, and excretion. These changes can lead to therapeutic failure or adverse drug reactions, necessitating individualized drug therapy. Objectives: This study systematically reviews and comparsses pharmacokinetic alterations of commonly used drugs in NCD patients, evaluating their impact on drug efficacy and safety, dose adjustments, and treatment optimization. Methods: A systematic review was conducted following PRISMA guidelines. Data from PubMed, Scopus, Embase, and Cochrane Library were analyzed. Studies reporting pharmacokinetic parameters in NCD patients were included. Risk of bias was assessed using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Results: Out of 4,000 studies screened, 120 met inclusion criteria. Key findings include delayed absorption (e.g., metformin in CKD), altered distribution (e.g., beta-blockers in hypertension), reduced metabolism (e.g., statins in liver disease), and impaired excretion (e.g., aminoglycosides in CKD). These variations necessitate dose adjustments to optimize therapy and prevent toxicity. Conclusion: Significant pharmacokinetic alterations in NCD patients necessitate individualized dosing strategies. Understanding these changes is crucial for improving drug efficacy, minimizing adverse effects, and optimizing pharmacotherapy. Further research on novel drugs is recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".