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Innovation management

What the Vienna Philharmonic Orchestra and biotech innovation have in common…

05 March 2013

How effective orchestration of resources can overcome the biomedical productivity crisis and unlock an African biotech revolution!

Productivity in biomedical R&D is a frequently discussed topic. Quality has been singled out as a major issue affecting productivity in the life and biomedical sciences alike; the issue is particularly rife in the technology-driven areas of Genomics and Proteomics.

I’d like to explore how R&D capacity can be organised differently, in a productivity enhancing manner. My ultimate goal is to make the case for an entirely different approach to managing Genomics and Proteomics infrastructure in Africa; and, to postulate that it is possible to achieve significant productivity gains on the continent, provided resources are utilised in a particular kind of manner.

Introduction

Imagine the orchestra pit at the ‘Musikverein’ in central Vienna (http://www.musikverein.at/). A conductor enters the pit and approaches the podium. As she walks past semi-circularly seated musicians, a feeling of uneasiness befalls her. One or two members of the brass ensemble seem to be missing; the tuba player looks as if sick to the bone. She looks at the string instrument section. The condition of cellos and contrabasses is appalling; the varnish on the instruments is coming off, and they are scratched all over. Some of the violins appear to have missing strings, and the lead violinist seems to be looking for her bow. In the centre of the pit, the flutes and woodwinds have difficulties in tuning their instruments. Do they actually know how to handle their sensitive instruments at all? There is a piano in the back but there is no one to play it. The audience is waiting. The conductor feels their excitement as she walks towards the conductor’s stand. What will she do? How will she be able to perform?

Such is the state of Genomics and Proteomics infrastructure in Africa. We have got some capacity, but nowhere at a critical mass. Sometimes, we have got physical assets, but no one to use them effectively. When we invest into creating human resources, we may not have the means to sustain them. When they decide to leave, they migrate elsewhere, for lack of local employment opportunities; we then end up benefiting foreign economies, instead of contributing towards the enhancement of our own. Where we do have infrastructure, it is often not managed properly. Worst of all, where capacity exists, it often does so in isolation. It serves a narrow scope, instead of being utilised in a value-maximising manner for entire regions, countries, or the continent.

When it comes to infrastructure, players in Africa are ready for competition. But, they are focused on battling local peers for scarce resources, rather than joining efforts into building distinctive competencies that have impact on a global scale. All of this tends to happen in face of well-known problems regarding the supply of electricity; the cost-effective provision of reagents; the availability of human resources; and the quality of preventative maintenance of equipment. The net effect is that investments into physical and human resources don’t yield proper returns in terms of scientific publications, patents, and biomedical innovation.

Occasionally, we see indicators of scientific greatness and even pockets of bio-economic activity. But, when we do, these are often generated with substantial contributions – financially or in-kind – from overseas. As well-intended and important as they are, they are only part of the story. We need to learn our own lessons, and make our own mistakes; we need to come up with our own, contextually relevant solutions.

Network orchestration 

Network orchestration is the art of assembling an array of resources into seamless strings of value generation; it is the art of conducting resources and capabilities in unison, to deliver outstanding value to beneficiaries. It is an approach that maximises resource utilisation, and it optimises the delivery of value.

Instead of concentrating on a single resource or capability, orchestration employs an entire network of resources; it assembles individual components into customised cascades of service and product delivery. Orchestration creates and renders networks agile and resilient to external and internal turbulence; it builds critical mass and economies of scale; it delivers products and services at high quality, fast, and – most of all – cost-effectively. It facilitates a self-policing approach, owing to the redundancy that can be built into the network. Where resources are missing, or failing to deliver, they can be replaced by alternates.

The net effect is a virtually integrated web of value providers who – according to their ability to contribute – combine efforts into generating products and services better, faster, cheaper and in much greater volumes.

Features of an ‘omics’ orchestra

Genomics and Proteomics are hallmark areas in today’s life and biomedical sciences. They are characterised by the use of rapidly evolving technologies, employed in the interest of converting biological materials (e.g. blood from cancer patients) into digital data. This is done at previously unknown levels of scale and complexity; it happens in the interest of converting research outputs into meaning and – consequently – into socio-economic value.

At no other point in the history of science have the principles of quality, turnaround (time), cost and volume been more relevant than in today’s industrialised era of biomedical research. Now, more than ever, are the principles of networking, cooperation and coordination essential ingredients of successful innovation in the life sciences. Where else, if applied properly, can these principles have more positive impact than in a resource-scarce environment, such as in many of the emerging economies in Africa? Where, if not in Africa, should the principles of networking and effective resource utilisation be more relevant?

Here are some of the principles we apply at the CPGR in building a viable ‘omics’ network:

  1. The unit of competitiveness is the network, not the individual entity. We can only reach levels of global competitiveness if we move from individual competition towards competition with networks established elsewhere in the world. We compete in an orchestrated manner.
  2. The orchestra, and its members, serves the interest of a beneficiary; in fact, it assembles in response to the need of a beneficiary.
  3. Beneficiaries are customers and end-users, enjoying short-term gains in the form of high-quality data, delivered faster, better, cheaper; and long-term benefits in the form of better diagnostic tests or personalized drug treatment, channeled through the innovation chain at ever decreasing levels of R&D cost and cycle time.
  4. All members of the orchestra are skillful artisans in their respective craft. The network composes of seasoned veterans and upstart talents. In ‘omics’ parlance, it employs mature technology platforms and hot, new, latest-stage technologies. These can be assembled into dedicated value propositions, as needed.
  5. Each player has an ability to perform individually but will only be able to maximise its potential by being part of the orchestra  The network renders support, as needed, and precipitates demand, if warranted.
  6. Individual players are part of proficiency testing schemes. Proficiency testing is carried out regularly and results are used to assess performance, and to facilitate selection of members.
  7. Learning and training (practicing) are hallmark features of the orchestra. Whatever knowledge is gained in one part of the network is transferred across the entire system, to ensure viability of the whole.
  8. Each player in the orchestra has at least three substitutes. This is to ensure a high level of redundancy, to enhance flexibility, and to render the network resilient.
  9. Members of the orchestra can provide capacity or intellectual property to the network. In other words, a member can be a supplier of time (in the interest of delivering a service, or developing a product), or knowledge (e.g. by way of transferring process know-how into the network, where it will be accessible to members, and a greater number of users).
  10. The benefit to users is in the economies of scale and scope the network creates; in rendering outputs in a cost-effective fashion; in the increased flexibility, owing to the diversity of the network; and, in the enhanced resilience due to redundant capacity, significantly reducing the chance of service delivery problems.

Conclusion

The conductor studies the results of a survey her Directorate has carried out amongst the orchestra’s audience over the last 12 months. When finished, she starts devising a concert plan for the upcoming season. From a pool of skilled musicians, she assembles a quartet: they will play Beethoven’s string quartets on Mondays; she puts together a chamber orchestra: they will embark on playing Vivaldi, Haendel, and Bach, on Wednesdays; lastly, she assembles an entire symphonic orchestra: they will focus on playing Anton Bruckner and Gustav Mahler, performing once a month.

Orchestration assembles resources in an agile, customer-focused fashion. It has the potential to create centers of critical mass and economies of scope and scale where previously there were none! It has the ability to enhance the development of bio-economies across the African continent through better resources utilization, coordination and concentration.

Addendum

Quality issues in biomedical research

I have previously dealt with the issue of quality and reproducibility (Hiller, 2012). So have others! The following list of examples is not exhaustive, it is merely meant to illustrate the known problems:

  • In 2002, a high-profile report of the use of mass spectrometry for the diagnosis of ovarian cancer turned out to be completely flawed because of poor experimental design (Petricoin, et al., 2002);
  • In 2004, a report found that ‘five of the seven largest published studies addressing cancer prognosis did not classify patients better than chance’ (Wacholder, Chanock, Garcia-Closas, & Rothman, 2004);
  • In 2011, a study showed that ‘an empirical assessment of 18 published papers of microarray studies showed that independent analysts could perfectly reproduce the results of only two of the studies, and it sometimes took over a month to reproduce a single figure’ (Ioannidis & Khoury, 2011);
  • Presently, an unspoken industry rule alleges that at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting. As a case in point, Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, a first-of-a-kind analysis on data irreproducibility found (Mullard, 2011).

Network orchestration

The concept of ‘process orchestration’ was originally introduced by John Hagel and John Seely Brown in ‘The Only Sustainable Edge: Why Business Strategy Depends on Productive Friction and Dynamic Specialization’ (Hagel & Brown, 23005). Earlier, Peter Drucker used the conductor and the orchestra to describe leadership with the flattening of the organisation. Today, the concept is applied by many companies, typically within the confines of outsourcing and extended global supply chains. For example, Boeing’s 777 jet is assembled from three million parts from more than 900 suppliers in 17 countries around the world. The movement from a traditional firm toward a network orchestrator requires a shift in focus from the firm to the network, a shift in management from control to empowerment, and a shift in value creation from specialization to integration. Despite being applied by in aspects by many companies across the world, network orchestration is epitomised by Chinese supply chain giant Li & Fung. Generated in the early 1900s, the company today manages more than 8.300 suppliers, 70 sourcing offices and operates in more than 40 countries (Fung, Fung, & Wind, 2008).

References 

Fung, V. K., Fung, W., & Wind, Y. (2008). Competing in a flat world: building enterprises for a borderless world. Wharton School Publishing.

Hagel, J., & Brown, J. S. (23005). The only sustainable edge: Why business strategy depends on productive friction and dynamic specialization. Harvard Business Press.

Hiller, R. (2012). On ‘omics’ translation and innovation. Retrieved 2013, from CPGR blog: http://www.cpgr.org.za/blogspot/?p=201

Ioannidis, J. P., & Khoury, M. J. (2011). Improving validation practices in “omics” research. Science, 334(6060), 1230-1232.

Mullard, A. (2011). Reliability of’new drug target’claims called into question. Nature Reviews Drug Discovery, 10(9), 643-644.

Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., et al. (2002). Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359(9306), 572–577.

Wacholder, S., Chanock, S., Garcia-Closas, M., & Rothman, N. (2004). Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. Journal of the National Cancer Institute, 96(6), 434-442.

Your organisation is supporting innovation. Really…?

‘Innovation’ is everywhere these days. When you Google ‘innovation’ you get more than 400 million results. There are more than 300.000 books that deal with ‘innovation’ available at Amazon.

Whether you play in this space or you have been exposed to the concept for the very first time, you will sooner or later be in touch with like-minded individuals at innovation agencies, centres, consultancies, clubs, happenings, hubs, or summits.

After a while, you’d come to believe that you must innovate, lest you, your company, or country, will lose its mojo and probably die…

I have been wondering, in view of all this hype, if anyone has considered properly what is required for innovation to happen.

Robert Hurley and Tomas Hult[1] have applied their minds to this and derived a set of innovation characteristics through comprehensive review of extant literature.

While I am not touching on definitions of innovation per se, I have used their findings to frame a set of questions relevant to the ‘innovativeness’ of organisations. Each of these can be read as a strategic choice that any organisation can make in forging an innovation path, ‘or die’.

Do you think your organisation is ready for the innovation journey?

  • Organisation size & resources: Does your organisation provide ample resources to overcome innovation barriers or are you struggling to find the bare essentials for getting your job done?
  • Age: Is your organisation old and bureaucratic or young and agile? Are you pushing paperwork or making swift decisions?
  • Differentiation of the organisation: Does your organisation promote diversity of worldviews or is it rather subscribing to a single dominant logic?
  • Low formalization  Is your organisation very formal or more like a shape-shifter?
  • Loose coupling, autonomy, and lack of hierarchy: Is your organisational structure hierarchical and rigid, or does it allow for adaptive self-organisation?
  • Market intelligence & market focus: Is your organisation responsive to the outside, or does it subscribe to a ‘must-invent-here’ and inward looking state of mind?
  • Planning: Does your organisation look far into the future or is its perspective rolling over from one year to the other?
  • Status differential: Is your organisation occupied with status and the fancy office for the executive, or does it put its attention to the task at hand? Is it ornamental or rather practical?
  • Learning & development: Does your organisation promote personal growth and development or does it treat you like a physical asset class?
  • Power sharing: Does power rest with one individual, or a few, or is information flowing freely, allowing dissenting opinions?
  • Participative decision making: Are you involved in planning and decision making or are these activities reserved for a privileged group of people and happening behind closed doors?
  • Support and collaboration: Is your organisational culture one of collegial support or is everybody out on their own?
  • Communication: Is information flowing freely across functions or is all you get ‘memos’ on a need-to-know basis?
  • Tolerance for conflict and risk taking: Is your organisation afraid of making mistakes or does it have the confidence to make decisions in the face of uncertainty? Does it encourage you to test a new idea or does it rather tell you ‘to shut up and do your job’?


[1] Robert F Hurley & G Tomas M Hult (1998) Innovation, market orientation, and organisational learning: An integration and empirical examination. Journal of Marketing, 62, 42-54.

 

Making (life science) innovation better

Emerging economies, such as South Africa, have a desire to become relevant players in promising new areas of economic growth. Biotechnology is one of these areas. In order to become relevant, therefore, these countries need to allocate adequate resources to innovation, failing which they will always remain on the receiving end of products and services developed elsewhere. However, in contrast to the developed world, emerging countries don’t have the luxury of ‘unlimited trial-and-error’ when it comes to allocating resources to innovation efforts. Biotech, in particular, has turned out to be increasingly complex, long-winded, costly and fraught with problems[1][2]. Therefore, alternative approaches to managing biotech innovation may be needed.

Of innovation and human activity systems

In his seminal book ‘Making work systems better’[3] Belgian systems practitioner Luc Hoboeke condenses prior insights of influential systems thinkers such as Peter Checkland[4] and Stafford Beer[5] into an original approach for the effective organisation of human activity systems. I am using some of his insights in exploring how life science innovation could be organised in a much better way.

In his book, Luc Hoboeke suggests that human activity systems are organised along two primary dimensions, time and level of process. Process levels must not be confused with a hierarchical organisation of activities. Rather, higher-order process levels create conditions for lower-order processes, similar to a Russian Doll (Matroschka) where the outer shell creates the raison d’être for the inner one. Time is essentially reflecting the necessary gap between the perception of need, its conversion into innovative insight, and the reduction of the latter into tangible products or services.

Across time, processes levels are organised into three fundamental domains, namely (i) added-value, (ii) innovation and (iii) value systems. The first one is where tangible products or services are rendered, value of which is perceived according to the four essential criteria of volume, price, quality and time (turn-around). The second domain is where perceptions of change (internal/external) are assimilated into the creation of new products and services, through adequate allocation of resources. The third domain (value systems) is where engagement with the environment takes place in such a manner that foundations for the sustainable future existence of an organisation in its respective environment are being created.

The more efficient these domains and process levels integrate over time, the more effective any innovation effort will be. Hoboeke’s model touches on two fundamental principles underlying innovation: (1) All human activity systems are made of communication. (2) Without communication, no work system has purpose; no effort aimed at innovation has meaning.

Life science innovation doesn’t work without communication either

In the innovation systems domain, Luc Hoboeke suggests the following qualities for measuring the effectiveness of any innovation effort:

  1. Desirability (D) – an attribute of a relation between innovators and stakeholders. It can be measured by the degree of positive effort that both make in that relation.
  2. Feasibility (F) – an attribute of the relation between innovators and stakeholders. It can be measured by the degree of defensive effort that both invest in the relation.
  3. Transferability (T) – the degree to which an innovation can easily be spread in the added-value domain gives an indication of its transferability.
  4. Systemicity (S) – the degree in which an innovation has been conceived, taking into account the interfaces with other areas, is an indicator of its systemicity.

(D) and (F) speak to the importance of proper stakeholder engagement. Without having a sense of what stakeholders need (D) and to what extent they may be opposed towards change (F), any efforts into innovation will be fruitless. (T) refers to the ability to reduce ‘ideas’ into products and services in an efficient manner. This requires that we have the means to convert ideas into specifications, plans and outcomes. (S) refers to the degree to which any innovation is ‘in touch’ with – more or less – related areas. For example, if the goal of an innovation effort is the development of a diagnostic test, we’d have to ask to what extent it impacts on the quality of patients’ lives, what impact it has in economic terms, how it could improve drug treatment, or – more specifically – how seamless it could be implemented into existing health delivery systems.

So, how does all of this apply to life science innovation? If we assume that some kind of basic or applied research is a driving force of innovation, what Hoboeke’s model suggests is that for this research to yield innovation outcomes, the corresponding activities must interface with relevant stakeholders already at the conceptual level. Engagement with such stakeholders, such as government departments, Pharmaceutical companies, clinical trial organisations, pathology laboratories, funders or investors, will go a long way to surface needs (D) and/or objections (F) regarding efforts aimed at this particular innovation. In other words, effective communication with stakeholders will essentially add purpose to otherwise potentially misguided innovation efforts, including early research initiatives. The same process will ensure that innovation outcomes have systemicity, that is, that they have impact beyond their ‘domain of origin’. With respect to Genomics driven innovation, what comes to mind are novel diagnostic tests that could be applied in routine laboratory settings; improved policies for the provision of drugs; or data that can be used to enhance design of clinical trials.

Translated into a resource-constrained environment, innovation efforts could be rationalized as follows:

  1. Innovators should have a good understanding of ‘problem areas’, such as health, and how these interact with others in systemic and possibly innovation-enhancing way.
  2. Likewise, researchers should consider how ‘information-rich’ discovery fits into other areas, such as drug development, Diagnostics (Dx) or health delivery.
  3. By way of using or creating adequate communication platforms, innovators should engage with relevant stakeholders in order to prioritize efforts that are desirable and feasible.
  4. Innovators need to have the means necessary for translating ‘ideas’ into value-adding products or services.

In order for the process to be effective in a developing economy, more significant effort should be put into communication and coordination prior to the creation or allocation of resources. While the latter may create a warm and fuzzy feeling of accomplishment, only the former will ensure that such resources are actually employed in an innovation-stimulating manner.

A mega-fund boost to biomedical innovation in Africa

A recent paper in Nature Biotechnology suggests that we could use creative financing techniques for funding biomedical innovation on a mega-scale. I argue that this could be a possible way for jump-starting the African biotech century …

‘Valley of death’ or ‘terra incognita’?

Considering the looming patent cliff (1), the broken block-buster drug business model (2), surging costs for drug development (3), increasing complexity of biological research, disappointing rates of success of ‘Translational’ initiatives (4), and the patchy, if not decreasing, financial returns on investments made by venture capital (VC) funds (5, 6), it should come with no surprise that many look at the prospects of the life science / biomedical industry with skepticism, if not in despair.

The ‘valley of death’ metaphor (7) is often used to describe the demise of a large fraction of early-stage biotechs who fail to make it because promising candidate molecules or exciting new diagnostic tests do not achieve critical performance criteria. The phenomenon has been known for a while, which is why governments all over the world are stepping in to invest strategically in this space in order to stimulate growth of their respective bio-economies.

The situation is no different for Africa, even though the problem here presents itself in a slightly different manner.  In Africa, to use the ‘valley of death’ metaphor is actually an oxymoron: It would assume that there is a large number of ventures that have tried and failed when entering the critical early stage of biomedical product development. In reality the number of such endeavors has been, and continues to be, so low that it is probably be more apt to speak of a terra incognita that remains largely unconquered on the African continent.

And yet, what makes Africa so interesting is that it combines urgent needs for biomedical innovation with a wealth of opportunities, such as in the form of professionals who are as ingenious as can be in view of the often challenging context (and often achieve remarkable successes with what little support they have got) and a biological diversity (human, animal, plant, microbial) that should make investments into biotech innovation hugely attractive, in principle (*).

What, then, can be done in order to fully realise the slumbering potential of an African biotech century?

Clever financing techniques to the rescue!

A recent paper in Nature Biotechnology (8) provides an interesting answer to this question.

Taking into account the rather dismal gains made by life sciences venture capital firms and the disappointingly low rate of investment into translational efforts over the last 10 years, the authors set out to propose a securitisation based approach to financing biomedical innovation (8).

Although creative mechanisms of financing, and securitisation in particular (9), are viewed with scepticism in the wake of the 2008 financial crisis, the authors argue that there is merit in exploiting such techniques for the benefit of much needed large-scale financing of biomedical innovation.

In essence, the approach they propose is composed of two components: (i) creating large diversified portfolios—‘megafunds’ on the order of $5–30 billion—of biomedical projects at all stages of development; and (ii) structuring the financing for these portfolios as combinations of equity and securitized debt so as to access much larger sources of investment capital (8).

This is based on a number of premises:

  1. Long-term annuities, such as in case of 10-15 year bonds, are more in sync with the typical life cycle of drug or diagnostic product development, as opposed to the 2-3 year horizons of VC and private equity investors (and, oddly enough, of some institutional investors too).
  2. A large-scale fund could cater to a broader range of investor ‘risk appetites’ by way of structuring debt (and equity, for the higher risk/higher return clientele) into different types of maturities (say, 5, 10, and 15 years). Consequently, these maturities will yield different returns. Although not comparable to rates of return desired by VCs, these will be attractive enough to large scale pension or sovereign funds.
  3. A megafund is able to attract capital from a diverse range of investors. In favor of this, the paper argues that ‘at the end of 2010 the California Public Employees Retirement System held $226 billion of investable assets, the Norwegian government pension fund held $537 billion and nongovernment US institutional money market funds held $1.1 trillion. Moreover, as of the end of 2010, the total size of the US bond market was $35.2 trillion. In relation to these magnitudes, a megafund of $30 billion no longer seems as unattainable if debt-financing is feasible’ (8).
  4. A bio-innovation fund of such a scale makes investments into the projects across the entire spectrum of the innovation chain feasible, from early-stage development to ‘close-to-market’ projects. The reason for this is that risk and uncertainty of success are diluted out due to the large number of programs undertaken simultaneously. Even though it may be impossible to predict which of these programs will succeed, or fail, the likelihood of success will increase with the number of programs undertaken. The authors go on to show that even at a predicted success rate of 5%, the megafund is viable and yields returns in the order of 5 and 8% for senior and junior obligations, respectively, and in the order of 9% for the equity portion of the fund (8). In simulations run by the authors, investors were repaid in > 99% of the time.

Notwithstanding practical challenges when creating such a megafund, there are a number of opportunities that make it look like an interesting solution for the creation of a bio-economy in the developing parts of the world. From a South African perspective, the following comes to my mind:

  1. Although the life and biomedical sciences private equity and VC sector is literally non-existent, the financial sector in South Africa is very strong. In fact, it’s probably one of the most resilient and stable sectors in comparison to others in the world, considering its ability to absorb the 2008 financial crisis shockwaves.
  2. There is a socio-political climate that supports, in principle, the creation of a bio-economy.
  3. Considering South Africa’s rich bio-diversity (human, animal, plant, microbial, etc), there is ample opportunity for ‘biological exploration and mining’ ventures.
  4. There are pressing health needs that warrant the development of fit-for-purpose solutions (in particular drugs and vaccines that are both affordable and efficacious), yielding socio-economic benefits in the mid- to long term. This, amongst others, is something that should appeal to ‘social responsibility’ minded investors.
  5. South Africa, and Africa, has a rapidly growing middle class, making the development of products for this geographical region an interesting prospect on the mid- to long-term.

In summary,  I think there is an interesting opportunity for players from the financial and the life sciences sectors to explore synergies and complementary expertise in creating innovative new ways for financing biomedical innovation in Africa…

Notes

* The obvious strategy for governments in Africa, assuming that there is a genuine interest in building a bio-economy, would be to invest strategically for a period of at least 10-15 years, in order to build the institutional capacity, and knowledge, necessary for biotech innovation. Amongst others, this would require that large-scale commitments are made into ramping up the number of early-stage investments and to concentrate on learning, not pushing for returns. Trying to pick the proverbial ‘close-to-market’ cherries won’t work, simply because, (i) achieving meaningful returns is a problem of size and statistics and, (ii), the success rates will only go up over time, if only marginally, due to the accumulation of institutional knowledge. The latter will only happen if significant amounts of investments were made in the first place. Following this (i.e. after an initial period of 10-15 years), they could then concentrate on luring private capital into that space, who will be more willing to come in view of a public show of confidence; and, when they can rely on access to relevant institutional knowledge at the same time.

References

1 ‘Patent Cliff’ Hits Europe Drug Makers (25 October 2012) http://online.wsj.com/article/SB10001424052970203897404578077882348809420.html

2 Lessons from Lipitor and the broken blockbuster drug model (10 December 2011) The Lancet, 378, 1976.

3 Roger Collier (2009) Drug development cost estimates hard to swallow. CMAJ, 180(3): 279–280.

4 On ‘omics’ translation and innovation http://www.cpgr.org.za/blogspot/?p=201

5 The Biotech Venture Capital Math Problem (15 March 2012) http://lifescivc.com/2012/03/the-biotech-venture-capital-math-problem/

6 When It Comes to Venture Funds, Small Is Still Beautiful (19 January 2012) http://thebij.com/2012/01/19/when-it-comes-to-venture-funds-small-is-still-beautiful/

7 Fixes in Financing – Financial Innovations for Translational Research. Milken Institute. http://www.milkeninstitute.org/events/events.taf?function=detail&ID=394&cat=finlabs

8 Jose-Maria Fernandez, Roger M Stein & Andrew W Lo (2012) Commercializing biomedical research through securitization techniques. Nature Biotechnology, 30: 964-975.

9 http://en.wikipedia.org/wiki/Securitization