Evidence map›Paper›PMID 40681832›Full record

ArticleDrug delivery and translational research2025

Traversing the Valley of Death for nanotechnology-based natural products: strategies and insights from pharmaceutical stakeholders.

Jian Sheng Loh, Chan Yew Low, Wei Peng Low, Yun Lin Ng, Wen Qi Mak, Li Kar Stella Tan, Hong Hao Chan, Yong Sze Ong, Stephanie Kay Ann Cheah, Emmellie Laura Albert and 1 more

Abstract read
In one paragraph

Article in Drug delivery and translational research, 2025. 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. Article
  4. Natural Products with Pharmaceutical Activities.Molecules (Basel, Switzerland) · 2025
    Article
  5. Review
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

11 authors.

Jian Sheng LohSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Chan Yew LowSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Wei Peng LowSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Yun Lin NgSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Wen Qi MakSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Li Kar Stella TanSchool of Pharmacy, Faculty of Health & Medical Sciences, Taylor's University, 1, Jalan Taylors, Subang Jaya, Selangor, 47500, Malaysia.
Hong Hao ChanSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Yong Sze OngSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Stephanie Kay Ann CheahSchool of Business and Economics, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, 47500, Malaysia.
Emmellie Laura AlbertSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia.
Chee Wun HowSchool of Pharmacy, Monash University Malaysia, Jalan Lagoon, Selatan Bandar Sunway, Selangor, 47500, Malaysia. How.CheeWun@monash.edu.ORCID http://orcid.org/0000-0002-5910-6542

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanotechnology-based natural products hold significant therapeutic promise by improving the bioavailability and efficacy of natural products. However, their translation from laboratory to clinic remains hindered by a complex interplay of strategic misalignment, regulatory complexity, and organisational hurdles, often leading to failure in the so-called “Valley of Death”. To understand the drivers of successful translation and commercialisation, this study engaged 16 pharmaceutical stakeholders through semi-structured, in-depth interviews. Thematic analysis revealed three critical enablers. First, product-market fit requires early integration of market analysis and need-driven innovation to ensure alignment with unmet medical needs and commercial viability. Moreover, effective articulation of value propositions is essential to translate technical performance into perceived value for both investors and end-users. Second, product differentiation and positioning are essential to communicate competitive advantage in a crowded and cost-sensitive market. Strategic use of comparators, value metrics, and strategic messaging are critical to demonstrate tangible benefits over existing formulations, while avoiding unnecessary complexity to preserve affordability. Third, cross-sector and horizontal collaborative networks across academia, industry, regulators, manufacturers, and peer companies play a vital role in overcoming translational challenges by fostering regulatory preparedness and pooling complementary expertise and resources. Building on these findings, we propose the NATURAL framework as a translational planning tool to support early decision-making by helping innovators anticipate key barriers, align strategies, and improve the likelihood of clinical translation. Altogether, this study offers actionable insights and a structured planning tool to de-risk and accelerate the translation of nano-based natural products from bench to market.

Indexed as

Biological ProductsNanotechnologyDrug IndustryHumansStakeholder ParticipationBiological ProductsCommercialisationNanotechnologyNatural productsPharmaceutical innovationTranslational frameworkValley of Death

Identifiers

PMID40681832
PMCPMC12508012

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.