Evidence map›Paper›PMID 33751365›Full record

ArticleJournal of pharmacokinetics and pharmacodynamics2021

Optimum multi-drug regime for compartment model of tumour: cell-cycle-specific dynamics in the presence of resistance.

Bharti Panjwani, Vijander Singh, Asha Rani, Vijay Mohan

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In one paragraph

Article in Journal of pharmacokinetics and pharmacodynamics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 25 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Bharti PanjwaniNetaji Subhas Institute of Technology, University of Delhi, Delhi, India.
Vijander SinghNetaji Subhas Institute of Technology, University of Delhi, Delhi, India.
Asha RaniNetaji Subhas Institute of Technology, University of Delhi, Delhi, India.
Vijay MohanSchool of Automation, Banasthali Vidyapith, Tonk, Rajasthan, India. mvijay@banasthali.ac.in.ORCID http://orcid.org/0000-0003-1457-2651
University of Delhi · INBanasthali University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work is focused on multi-objective optimisation of a multi-drug chemotherapy schedule for cell-cycle-specific cancer treatment under the influence of drug resistance. The acquired drug resistance to chemotherapeutic agents is incorporated into the existing compartmental model of breast cancer. Furthermore, the toxic effect of drugs on healthy cells and overall drug concentration in the patient body are also constrained in the proposed model. The objective is to determine the optimal drug schedule according to the patient's physiological condition so that the tumour burden is minimised. A multi-objective optimisation algorithm, non-dominated sorting genetic algorithm-II (NSGA-II) is utilised to solve the problem. The obtained results are thoroughly analysed to illustrate the impact of drug resistance on the treatment. The capability of optimised schedules to deal with parametric uncertainty is also analysed. The drug schedules obtained in this work align well with the clinical standards. It is also revealed that the NSGA-II optimised drug schedule with proper rest period between successive dosages yields the minimum cancer load at the end of the treatment.

Indexed as

AlgorithmsAntineoplastic Combined Chemotherapy ProtocolsCell CycleDrug Resistance, NeoplasmHumansModels, StatisticalNeoplasmsCancer chemotherapyCell cycle specificityDrug resistanceNon-dominated sorting genetic algorithm II

Identifiers

PMID33751365
OpenAlexW3136649676

What OpenQuestion holds

Textmetadata
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.