Ollie Osunkunle's Profile

Bioprinting has made remarkable progress in recent years, with Organovo making faster strides in the engineering of solid organs than many had predicted (1).

As Alexandre mentions, the rate of progress may have exceeded the ethical and regulatory frameworks concerning bioprinting. Artificial organs and tissues offer numerous benefits such as reduced use of animal testing, greater success in clinical trials, personalized drug testing and disease modelling (2). With this comes a number of ethical considerations in the use of living cells.

With regards to neural tissue bioprinting, the long-term applications are more likely to be for tissue insertions to facilitate repair of the CNS (regeneration or replacement of damaged cells) from neurodegenerative or traumatic injuries (3), rather than whole lobe or brain transplants.

With regards to the ethics of such treatment and specifically its impact on personal identity, it is important to consider the relationship between the anatomical structure of the brain, i.e. its structural identity, and its neurophysiological and psychological functions, i.e. its functional identity. Specifically, structural brain identity does not necessarily imply functional identity and vice versa. As an example, patients can suffer severe anatomical lesions without detectable functional alterations. Conversely, there are diseases of “functional identity”, which – at least as of yet – have no identifiable structural cause, such as certain psychiatric disorders (4).

Within this context, I think that the emphasis should be on accelerating the development of these technologies with the promise of much benefit in the near term. It is important to ensure an appropriate framework to deal with the more pressing ethical challenges such as access issues and stem cell management. Managing some of the media hype and more far-fetched considerations will help the public to understand the value of these advances.

1. Murphy SV, Atala A. 3D bioprinting of tissues and organs. Nat Biotechnol. 2014;32(8):773.
2. Ong CS, Yesantharao P, Huang CY, Mattson G, Boktor J, Fukunishi T, et al. 3D bioprinting using stem cells. Pediatr Res. 2018 Jan;83(1–2):223–31.
3. Hsieh F-Y, Lin H-H, Hsu S. 3D bioprinting of neural stem cell-laden thermoresponsive biodegradable polyurethane hydrogel and potential in central nervous system repair. Biomaterials. 2015 Dec 1;71:48–57.
4. Northoff G. Do brain tissue transplants alter personal identity? Inadequacies of some “standard” arguments. J Med Ethics. 1996 Jun 1;22(3):174–80.

Fascinating article. As Svernadi mentioned, demonstrating the efficacy of the OIDD programme is critical to ensuring its long-term viability. However, given the difference in the nature of a traditional sequential model of early drug discovery R&D, it is not clear that usual models of assessing R&D productivity, focused primarily on economic returns, would be the best approach here (1).

Carroll et al., propose a balanced dashboard of leading and lagging indicators across four categories to assess the success of an open innovation approach. These include investment, returns (in terms of hypothesis tested, new research conclusions etc.), pipeline health and culture & capabilities.

Across these categories, the OIDD program, as assessed in 2017, compares favourably with more traditional R&D programs. It has generated over 1.8m datapoints as a result of the testing of almost 50,000 crowdsourced compounds, yielding ~4% actives across projects – a comparable success rate to typical early drug discovery programs for an investment of $150 per compound (1).

Hopefully, this combination of a balanced view of assessing the success of open innovation R&D programs and the promise shown by programs such as Eli Lilly will encourage more biopharmaceutical companies to embrace open innovation.

1. Carroll GP, Srivastava S, Volini AS, Piñeiro-Núñez MM, Vetman T. Measuring the effectiveness and impact of an open innovation platform. Drug Discov Today. 2017 May 1;22(5):776–85.

Fascinating article – the progress in the field of regenerative medicine in the past few decades has been outstanding. 3-d bioprinting offers another step-change in the ability to bridge the gap between artificially engineered tissues and native constructs by virtue of the versatility to co-deliver cells and biomaterials with precise control over the end-state architecture.

As recently as 2014, the production of solid organs was considered a long-term aspiration, a challenge which Organovo appears to have tackled admirably (1). However, I would be eager to see more progress made with other solid organ types which have their own unique engineering challenges (such as the lack of the regenerative capacity of liver cells, amongst others).

1. Murphy SV, Atala A. 3D bioprinting of tissues and organs. Nat Biotechnol. 2014;32(8):773.

Unfortunately, as we move from to the ‘future of health through crowd-sourcing’ and a greater trend for participatory medicine, I fear that many of the issues encountered in traditional research models are again rearing their head.

In addition to the ethical and privacy issues discussed – a key question for me is how this model can ensure it has a demographically representative data sample? Even in traditional, cancer research, we know that despite the importance of diversity, trials are more likely to be conducted on people that are more male, white and younger than the demographic distribution of cancer sufferers (1).

The model of 23andMe, with a relatively high price for its recommended direct to consumer genetic test ($199 at the time of writing (2)), seems less likely to facilitate equal representation. Moreover, we know that awareness of direct to consumer genetic testing is lower amongst populations with lower incomes and lower numeracy skills (3).

Given the likely unrepresentative sample of 23andMe’s genomic sample, and a business model that seems unlikely to facilitate equal participation, I would be concerned by the inequity inherent in seeking innovation and research advances using primarily their data.

1. Murthy VH, Krumholz HM, Gross CP. Participation in Cancer Clinical Trials: Race-, Sex-, and Age-Based Disparities. JAMA. 2004 Jun 9;291(22):2720–6.
2. 23andMe. DNA Genetic Testing & Analysis – 23andMe [Internet]. [cited 2018 Nov 15]. Available from: https://www.23andme.com/
3. Agurs-Collins T, Ferrer R, Ottenbacher A, Waters EA, O’Connell ME, Hamilton JG. Public Awareness of Direct-to-Consumer Genetic Tests: Findings from the 2013 U.S. Health Information National Trends Survey. J Cancer Educ. 2015 Dec 1;30(4):799–807.

Considering the factors that will influence patient/consumer perceptions of diagnostic support tools is crucial in evaluating their rate of adoption (1).

On the one hand, we know that there is a gap between the knowledge accumulated in most domains of medicine (and codified in guidelines) and the actual care delivered by physicians (2). Clinical decision-support tools can play a critical role in addressing this gap, especially if they are embraced and effectively used by physicians as part of their workflow.

However, as mentioned in this article, the push to commercialization of these technologies with unproven efficacy risks damaging patient/consumer and physician confidence. Given the critical impact that a diagnosis can have, for a patient that is aware of a tool’s use, the lack of confidence would span across specialties (radiology and oncology). Perhaps then, the question is not strictly of specialty but of efficacy. As a patient, how would I feel about my physician using a diagnostic support tool? Great – but only if it works!

1. Herzlinger RE. Why innovation in health care is so hard. Harv Bus Rev. 2006;84(5):58.
2. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality. J Am Med Inform Assoc. 2003 Nov 1;10(6):523–30.

I have no doubt that the paradigm of care (surgery 4.0), as described is attainable. However, my contention is that the rate of adoption of these technologies will be slower than most anticipate.

The adoption of advances that are beneficial to patients, can be stalled by surgeons when interests are not aligned. Figure 1 in the article references surgery 2.0 which included laparoscopic and minimally invasive surgery. However, these advances although beneficial for patients were resisted by the general surgery community because of their effect on reducing the learning barrier to performing some operations. You no longer needed a general surgeon to perform the procedure, radiologists and others became as capable of delivering the treatment (1). Surgery 4.0 attempts to reduce the learning barrier and I fear may suffer a similar reaction, if insufficient care is placed in its promotion.

In a world where access to laparoscopic struggle is still a challenge in developing countries (2), driven largely by equipment costs and a lack of personnel, I struggle to imagine mass impact from the developments in surgery 4.0. Although the pricing mechanism you describe improves the trialability of the technology, its long-term affordability is key for price-sensitive regions. Rather than delivering surgery to the 5.0 billion people that desperately need care, I would contend that it is likely to remain the preserve of innovative surgeons at leading academic centres for some time to come.

2030, is just over a decade from now – a brief flicker of time in the medical world, and in my view too short a time-frame to expect a dramatic change in care delivery without significant focus on addressing the major barriers to healthcare innovation.

1. Herzlinger RE. Why innovation in health care is so hard. Harv Bus Rev. 2006;84(5):58.
2. The role of laparoscopic surgery in developing countries: A review – SAGES Abstract Archives [Internet]. SAGES. [cited 2018 Nov 13]. Available from: https://www.sages.org/meetings/annual-meeting/abstracts-archive/the-role-of-laparoscopic-surgery-in-developing-countries-a-review/