Organ-on-a-chip platforms for the study of Traditional Chinese Medicine

For centuries, traditional Chinese medicines (TCMs) have been used for disease treatment and interest in TCMs is growing globally. There are thousands of classic TCM formulas that provide an enormous reservoir to search for potent drugs. A typical example is the discovery of artemisinin from TCMs that inhibits Malaria by YouYou Tu (a 2015 Nobel laureate). However, despite a strong desire to explore the scientific basis for the action of numerous Chinese herbs and TCM formulas, knowledge has been limited due to the complexity of TCM and the lack of the progress in modernization of TCM research.

Humanised, 3D tissue models are finding interest due to current overly-simplified immortal cell lines and non-human in vivo models providing poor prediction of drug safety, dosing and efficacy; 43% of drug fails are not predicted by traditional screening and move into phase I clinical trials1. Phase I sees a 48% success rate, phase II a 29% success rate and phase III a 67% success rate [1]. The drug development pipeline is pressurised due to adoption of high throughput screening / combinatorial libraries. However, while R&D spend has increased to meet this growing screening programme, success, measured by launched drugs, remains static [2]. This poor predictive power of the >1 million animals used in the UK each year drives the 12-15 year, £1.85B pipeline, for each new drug launch [3]. Contract research organisations (CROs) are also similarly hit by these problems.

Drive to reduce animal experimentation in toxicology and outright banning of animal testing for e.g. cosmetics in the UK has driven companies to outsource or to adopt the limited number of regulator approved NAT models for e.g. skin [4,5].

Another key area that uses 3D tissues is the field of advanced therapeutic medicinal products (ATMPs), i.e. tissue engineering/regenerative medicine. Regulation is a major ATMP bottleneck. It is thus noteworthy that regulators, such as the UKs Medicines and Healthcare Products Regulatory Agency (MHRA), are receptive to the inclusion of NAT-based data in investigative medicinal product dossiers [6].

The lifETIME CDT will directly address these issues through nurturing of a cohort training not only in the research skills required to conceive and design new NATs, but also in skills based on:

- GMP and manufacture.
- Commercialisation and entrepreneurship.
- Regulation.
- Drug discovery and toxicology - a focus on the end product.
- Policy.
- Public engagement.

Our NAT graduate community will impact on:

- Pharma - access to skills that develop tools to unlock their drug discovery and testing portfolios. By helping train graduates who can create and deploy NATs, they will increase efficiency of drug development pipelines.

- ATMP manufacturers - the same skills and tools used to deliver NAT innovation will help to deliver tissue engineered / combination product ATMPs.

- CROs - access to skills to create platform tools providing more sophisticated approaches to the diverse research challenges they face.

- Catapult Centres - access to skills that provide innovation that can be deployed across the broader healthcare sector.

- Regulatory agencies e.g. MHRA - better education for the next generation of scientists on development of investigational new drug / medicinal product dossiers to speedup approvals.

- Clinicians and NHS - access to more medicines more quickly through provision of highly skilled scientists, manufacturers and regulators. NATs will help drive the stratified/personalised medicine revolution and understand safety and efficacy parameters in human-relevant tissues. Clinicians will also benefit from development of ATMP-based regenerative medicine.

- Patients - benefit from skills for faster and more economically streamlined development of new medicines that will improve lifespan and healthspan.

- Public and Society - benefit from the economic growth of a thriving drug development industry. Benefits will be direct, via jobs creation and access to wider and more targeted healthcare products; and indirect, via increased economic benefit of patients returning to work and increased tax revenues, that in turn feed back into the healthcare systems.


[1]. Cook. Nat Rev Drug Discov 13, 419-431 (2014).
[2]. Pammolli. Nat Rev Drug Discov 10, 428-438 (2011).
[3]. DiMasi. Health Econ 47, 20-33 (2016).
[4]. Cotovio. Altern Lab Anim 33, 329-349 (2005).
[5]. Kandarova. Altern Lab Anim 33, 351-367 (2005).
[6]. https://goo.gl/i6xbmL