Development of Novel Micellar-Enhanced High-Throughput Microwell Spectrofluorimetric Method for Quantification of Lorlatinib: Application to In Vitro Drug Release and Analysis of Urine Samples.

Abdullah M Al-Hossaini, Hany W Darwish, Ahmed H Bakheit, Ibrahim A Darwish

Open access · goldAbstract read

In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact, top 89% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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 1 institution in 1 country.

Abdullah M Al-HossainiDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia.

Hany W DarwishDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia.

Ahmed H BakheitDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia.

Ibrahim A DarwishDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia.ORCID 0000-0003-3821-623X

King Saud University · SA

Funding

Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia RSPD2023R812

6 · The paper itself

Abstract

Lorlatinib (LOR) is a third-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor drug. The Food and Drug Administration (FDA) has granted an approval for the use of LOR as a first therapeutic intervention for individuals diagnosed with ALK-positive metastatic and advanced non-small-cell lung cancer (NSCLC). The present study outlines, for the first time, the development and validation of an innovative microwell-based spectrofluorimetric (MW-SFL) method for the quantification of LOR. The proposed method involved the enhancement of the weak native fluorescence of LOR by its micellization into the sodium lauryl sulfate (SLS) micelles. The procedures of the method were conducted in white opaque plates with 96 microwells, and the enhanced fluorescence signals were measured by a fluorescence plate reader at 405 nm after excitation at 310 nm. The measured relative fluorescence intensity (RFI) had a linear relationship with LOR concentrations in the range of 60-1600 ng mL

Indexed as

in vitro drug releaseLorlatinibmicrowell-based methodsspectrofluorimetryurine analysis

Identifiers

PMID37765067

DOI10.3390/ph16091260

PMCPMC10535339

OpenAlexW4386484197

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.