Evidence map›Paper›PMID 33126622›Full record

ReviewPharmaceutics2020

Timeline of Translational Formulation Technologies for Cancer Therapy: Successes, Failures, and Lessons Learned Therefrom.

Alexandre Pérez-López, Cristina Martín-Sabroso, Ana Isabel Torres-Suárez, Juan Aparicio-Blanco

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. 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.

Alexandre Pérez-LópezDepartment of Pharmaceutics and Food Technology, Faculty of Pharmacy, Complutense University of Madrid, 28040 Madrid, Spain.
Cristina Martín-SabrosoDepartment of Pharmaceutics and Food Technology, Faculty of Pharmacy, Complutense University of Madrid, 28040 Madrid, Spain.
Ana Isabel Torres-SuárezDepartment of Pharmaceutics and Food Technology, Faculty of Pharmacy, Complutense University of Madrid, 28040 Madrid, Spain.ORCID 0000-0003-2468-6177
Juan Aparicio-BlancoDepartment of Pharmaceutics and Food Technology, Faculty of Pharmacy, Complutense University of Madrid, 28040 Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past few decades, the field of cancer therapy has seen a significant change in the way in which formulations are designed and developed, resulting in more efficient products that allow us to ultimately achieve improved drug bioavailability, efficacy, and safety. However, although many formulations have entered the market, many others have fallen by the wayside leaving the scientific community with several lessons to learn. The successes (and failures) achieved with formulations that have been approved in Europe and/or by the FDA for the three major types of cancer therapy (peptide-based therapy, chemotherapy, and radiotherapy) are reviewed herein, covering the period from the approval of the first prolonged-release system for hormonal therapy to the appearance of the first biodegradable microspheres intended for chemoembolization in 2020. In addition, those products that have entered phase III clinical trials that have been active over the last five years are summarized in order to outline future research trends and possibilities that lie ahead to develop clinically translatable formulations for cancer treatment.

Indexed as

chemotherapydrug deliveryimplantsmarket approvalmedical devicesmicrospheresnanomedicinepeptide-based therapyradiotherapytranslational medicine

Identifiers

PMID33126622
PMCPMC7692572

What OpenQuestion holds

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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.