Evidence map›Paper›PMID 34702990›Full record

ReviewNature reviews. Drug discovery2022

Harnessing the predictive power of preclinical models for oncology drug development.

Alexander Honkala, Sanjay V Malhotra, Shivaani Kummar, Melissa R Junttila

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Drug discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 2 pooled it
–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

66 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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6 more citing papers are in PubMed but not listed here.

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.

Alexander HonkalaDepartment of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
Sanjay V MalhotraDepartment of Cell Development & Cancer Biology, Oregon Health & Science University, Portland, OR, USA.
Shivaani KummarCenter for Experimental Therapeutics, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA. kummar@ohsu.edu.ORCID 0000-0001-6906-1627
Melissa R JunttilaORIC Pharmaceuticals, South San Francisco, CA, USA. melissa.junttila@oricpharma.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

Indexed as

Drug DevelopmentMolecular Targeted TherapyAnimalsAntineoplastic AgentsBiomarkers, TumorDrug Evaluation, PreclinicalHumansNeoplasmsAntineoplastic AgentsBiomarkers, Tumor

Identifiers

What OpenQuestion holds

Textmetadata
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.