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You can book a party / event over the phone or in the venue. Please make sure you book in advance to avoid disappointment.

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Parties are for up to 30 kids but if you would like more spaces please speak to a member f the team.

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We can provide a choice of differenet packages.

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We have a range of different themed parties, including Ninja, Gymnastic, VIP and Super hero.

How long will children get to play in the gym?

For each party we allocate an hour of gym time and half an hour in our VIP seating area with games area included.


Arrowsmith, John. 2011. “Phase II Failures: 2008–2010.” Nature Reviews Drug Discovery 10 (5): 328–29. https://doi.org/10.1038/nrd3439.

Abstract: Not available.

Link: https://doi.org/10.1038/nrd3439.

Arrowsmith, John, and Philip Miller. 2013. “Phase II and Phase III Attrition Rates 2011–2012.” Nature Reviews Drug Discovery 12 (8): 569–569. https://doi.org/10.1038/nrd4090.

Abstract: Not available.

Link: https://doi.org/10.1038/nrd4090.

Beall, Reed F., Thomas J. Hwang, and Aaron S. Kesselheim. 2019. “Pre-Market Development Times for Biologic versus Small-Molecule Drugs.” Nature Biotechnology 37 (7): 708–11. https://doi.org/10.1038/s41587-019-0175-2.

Abstract: Not available.

Link: https://doi.org/10.1038/s41587-019-0175-2.

Dahlin, E., G. M. Nelson, M. Haynes, and F. Sargeant. 2016. Success rates for product development strategies in new drug development. J Clin Pharm Ther, 41: 198-202. doi:10.1111/jcpt.12362

Abstract: What is known and objective: While research has examined the likelihood that drugs progress across phases of clinical trials, no research to date has examined the types of product development strategies that are the most likely to be successful in clinical trials. This research seeks to identify the strategies that are most likely to reach the market—those generated using a novel product development strategy or strategies that combine a company’s expertise with both drugs and indications, which we call combined experience strategies. Methods: We evaluate the success of product development strategies in the drug development process for a sample of 2562 clinical trials completed by 406 US pharmaceutical companies. To identify product development strategies, we coded each clinical trial according to whether it consisted of an indication or a drug that was new to the firm. Accordingly, a clinical trial that consists of both an indication and a drug that were both new to the firm represents a novel product development strategy; indication experience is a product development strategy that consists of an indication that a firm had tested previously in a clinical trial, but with a drug that was new to the firm; drug experience is a product development strategy that consists of a drug that the firm had prior experience testing in clinical trials, but with an indication that was new to the firm; combined experience consists of both a drug and an indication that the firm had experience testing in clinical trials. Success rates for product development strategies across clinical phases were calculated for the clinical trials in our sample. Results and discussion: Combined experience strategies had the highest success rate. More than three and a half percent (0 036) of the trials that combined experience with drugs and indications eventually reached the market. The next most successful strategy is drug experience (0 025) with novel strategies trailing closely (0 024). Indication experience strategies are the least successful (0 008). These differences are statistically significant. What is new and conclusion: The primary contribution of this study is that product development strategies combining experience with drugs and indications strategies are the most likely to reach the market, even though they are least common strategy. Therefore, combined experience strategies remain underutilized. The findings also suggest a promising path for pursuing combined experience strategies: gaining expertise with drugs is likely to be a more effective path to gaining the expertise necessary for developing subsequent recombination strategies.

Link: doi:10.1111/jcpt.12362

Davis, Matthew M., Amy T. Butchart, John R.C. Wheeler, Margaret S. Coleman, Dianne C. Singer, and Gary L. Freed. “Failure-to-Success Ratios, Transition Probabilities and Phase Lengths for Prophylactic Vaccines versus Other Pharmaceuticals in the Development Pipeline.” Vaccine 29, no. 51 (November 2011): 9414–16. https://doi.org/10.1016/j.vaccine.2011.09.128.

Abstract: Research and development of prophylactic vaccines carries a high risk of failure. In the past, industry experts have asserted that vaccines are riskier to produce than other pharmaceuticals. This assertion has not been critically examined. We assessed outcomes in pharmaceutical research and development from 1995 to 2011, using a global pharmaceutical database to identify prophylactic vaccines versus other pharmaceuticals in preclinical, Phase I, Phase II, or Phase III stages of development. Over 16 years of follow-up for 4367 products (132 prophylactic vaccines; 4235 other pharmaceuticals), we determined the failure-to-success ratios for prophylactic vaccines versus all other products. The overall ratio of failures to successes for prophylactic vaccines for the 1995 cohort over 16 years of follow-up was 8.3 (116/14) versus 7.7 (3650/475) for other pharmaceuticals. The probability of advancing through the development pipeline at each point was not significantly different for prophylactic vaccines than for other pharmaceuticals. Phase length was significantly longer for prophylactic vaccines than other pharmaceuticals for preclinical development (3.70 years vs 2.80 years; p < .0001), but was equivalent for all 3 human clinical trial phases between the two groups. We conclude that failure rates, phase transition probabilities, and most phase lengths for prophylactic vaccines are not significantly different from those of other pharmaceutical products, which may partially explain rapidly growing interest in prophylactic vaccines among major pharmaceutical manufacturers.

Link: https://doi.org/10.1016/j.vaccine.2011.09.128.

DiMasi, J. “Risks in New Drug Development: Approval Success Rates for Investigational Drugs.” Clinical Pharmacology & Therapeutics 69, no. 5 (May 2001): 297–307. https://doi.org/10.1067/mcp.2001.115446.

Abstract: Not available.

Link: https://doi.org/10.1067/mcp.2001.115446.

Abrantes-Metz, Rosa M., Christopher Adams, and Albert D. Metz. 2004. “Pharmaceutical Development Phases: A Duration Analysis.” SSRN Electronic Journal, 2004. https://doi.org/10.2139/ssrn.607941.

Abstract: This paper estimates a duration model of late stage drug development in the pharmaceutical industry using publicly available data. The paper presents descriptive results on the estimated relationship between a particular drug's characteristics such as therapy category, route of administration and originator's size, and that drug's pathway through the three stages of human clinical trials and regulatory review. The results suggest that drugs with longer durations are less likely to succeed, drugs from larger firms are more likely to succeed and faster in the later phases of development, and that durations fell between 1995 and 2002.

Link: https://doi.org/10.2139/ssrn.607941

Arrowsmith, John. 2011. “Phase III and Submission Failures: 2007–2010.” Nature Reviews Drug Discovery 10 (2): 87–87. https://doi.org/10.1038/nrd3375.

Abstract: Not available

Link: https://doi.org/10.1038/nrd3375.

DiMasi, J A, L Feldman, A Seckler, and A Wilson. “Trends in Risks Associated With New Drug Development: Success Rates for Investigational Drugs.” Clinical Pharmacology & Therapeutics 87, no. 3 (March 2010): 272–77. https://doi.org/10.1038/clpt.2009.295.

Abstract: This study utilizes both public and private data sources to estimate clinical phase transition and clinical approval probabilities for drugs in the development pipelines of the 50 largest pharmaceutical firms (by sales). The study examined the development histories of these investigational compounds from the time point at which they first entered clinical testing (1993–2004) through June 2009. The clinical approval success rate in the United States was 16% for self‐originated drugs (originating from the pharmaceutical company itself) during both the 1993–1998 and the 1999–2004 subperiods. For all compounds (including licensed‐in and licensed‐out drugs in addition to self‐originated drugs), the clinical approval success rate for the entire study period was 19%. The estimated clinical approval success rates and phase transition probabilities differed significantly by therapeutic class. The estimated clinical approval success rate for self‐originated compounds over the entire study period was 32% for large molecules and 13% for small molecules. The estimated transition probabilities were also higher for all clinical phases with respect to large molecules.

Link: https://doi.org/10.1038/clpt.2009.295.

DiMasi, Joseph A., Henry G. Grabowski, and Ronald W. Hansen. 2016. “Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs.” Journal of Health Economics 47 (May): 20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012.

Abstract: The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars). Link: https://doi.org/10.1016/j.jhealeco.2016.01.012.

Dowden, Helen, and Jamie Munro. 2019. “Trends in Clinical Success Rates and Therapeutic Focus.” Nature Reviews Drug Discovery 18 (7): 495–96. https://doi.org/10.1038/d41573-019-00074-z.

Abstract: Not available.

Link: https://doi.org/10.1038/d41573-019-00074-z.

Harrison, Richard K. 2016. “Phase II and Phase III Failures: 2013–2015.” Nature Reviews Drug Discovery 15 (12): 817–18. https://doi.org/10.1038/nrd.2016.184.

Abstract: Not available. Link: https://doi.org/10.1038/nrd.2016.184.

Hay, Michael, David W Thomas, John L Craighead, Celia Economides, and Jesse Rosenthal. “Clinical Development Success Rates for Investigational Drugs.” Nature Biotechnology 32, no. 1 (January 2014): 40–51. https://doi.org/10.1038/nbt.2786.

Abstract: Not available.

Link: https://doi.org/10.1038/nbt.2786.

Hwang, Thomas J., Daniel Carpenter, Julie C. Lauffenburger, Bo Wang, Jessica M. Franklin, and Aaron S. Kesselheim. 2016. “Failure of Investigational Drugs in Late-Stage Clinical Development and Publication of Trial Results.” JAMA Internal Medicine 176 (12): 1826–33. https://doi.org/10.1001/jamainternmed.2016.6008.

Abstract: Importance: Many investigational drugs fail in late-stage clinical development. A better understanding of why investigational drugs fail can inform clinical practice, regulatory decisions, and future research. Objective: To assess factors associated with regulatory approval or reasons for failure of investigational therapeutics in phase 3 or pivotal trials and rates of publication of trial results. Design, Setting, and Participants: Using public sources and commercial databases, we identified investigational therapeutics that entered pivotal trials between 1998 and 2008, with follow-up through 2015. Agents were classified by therapeutic area, orphan designation status, fast track designation, novelty of biological pathway, company size, and as a pharmacologic or biologic product. Main Outcomes and Measures: For each product, we identified reasons for failure (efficacy, safety, commercial) and assessed the rates of publication of trial results. We used multivariable logistic regression models to evaluate factors associated with regulatory approval. Results: Among 640 novel therapeutics, 344 (54%) failed in clinical development, 230 (36%) were approved by the US Food and Drug Administration (FDA), and 66 (10%) were approved in other countries but not by the FDA. Most products failed due to inadequate efficacy (n = 195; 57%), while 59 (17%) failed because of safety concerns and 74 (22%) failed due to commercial reasons. The pivotal trial results were published in peer-reviewed journals for 138 of the 344 (40%) failed agents. Of 74 trials for agents that failed for commercial reasons, only 6 (8.1%) were published. In analyses adjusted for therapeutic area, agent type, firm size, orphan designation, fast-track status, trial year, and novelty of biological pathway, orphan-designated drugs were significantly more likely than nonorphan drugs to be approved (46% vs 34%; adjusted odds ratio [aOR], 2.3; 95% CI, 1.4-3.7). Cancer drugs (27% vs 39%; aOR, 0.5; 95% CI, 0.3-0.9) and agents sponsored by small and medium-size companies (28% vs 42%; aOR, 0.4; 95% CI, 0.3-0.7) were significantly less likely to be approved. Conclusions and Relevance: Roughly, half of investigational drugs entering late-stage clinical development fail during or after pivotal clinical trials, primarily because of concerns about safety, efficacy, or both. Results for the majority of studies of investigational drugs that fail are not published in peer-reviewed journals.

Link: https://doi.org/10.1001/jamainternmed.2016.6008.

Keyhani, Salomeh, Marie Diener-West, and Neil Powe. “Are Development Times For Pharmaceuticals Increasing Or Decreasing?” Health Affairs 25, no. 2 (March 2006): 461–68. https://doi.org/10.1377/hlthaff.25.2.461.

Abstract: This study examines trends in drug development times. Longer clinical trial times have been described as one factor leading to higher drug prices. Previous reports on development times have been based on proprietary data. We examined trends in development times for 168 drugs with data collected from publicly available sources. The median clinical trial and regulatory review periods for drugs approved between 1992 and 2002 were 5.1 and 1.2 years, respectively. Clinical trial periods have not increased during this time frame, and regulatory review periods have decreased. Therefore, it is unlikely that longer clinical trial times are contributing to rising prescription drug prices.

Link: https://doi.org/10.1377/hlthaff.25.2.461.

Kola, Ismail, and John Landis. “Can the Pharmaceutical Industry Reduce Attrition Rates?” Nature Reviews Drug Discovery 3, no. 8 (August 1, 2004): 711–16. https://doi.org/10.1038/nrd1470.

Abstract: The pharmaceutical industry faces considerable challenges, both politically and fiscally. Politically, governments around the world are trying to contain costs and, as health care budgets constitute a very significant part of governmental spending; these costs are the subject of intense scrutiny. In the United States, drug costs are also the subject of intense political discourse. This article deals with the fiscal pressures that face the industry from the perspective of R&D. What impinges on productivity? How can we improve current reduced R&D productivity?

Link: https://doi.org/10.1038/nrd1470.

Lauer, Michael S., David Gordon, Gina Wei, and Gail Pearson. 2017. “Efficient Design of Clinical Trials and Epidemiological Research: Is It Possible?” Nature Reviews Cardiology 14 (8): 493–501. https://doi.org/10.1038/nrcardio.2017.60.

Abstract: Randomized clinical trials and large-scale, cohort studies continue to have a critical role in generating evidence in cardiovascular medicine; however, the increasing concern is that ballooning costs threaten the clinical trial enterprise. In this Perspectives article, we discuss the changing landscape of clinical research, and clinical trials in particular, focusing on reasons for the increasing costs and inefficiencies. These reasons include excessively complex design, overly restrictive inclusion and exclusion criteria, burdensome regulations, excessive source-data verification, and concerns about the effect of clinical research conduct on workflow. Thought leaders have called on the clinical research community to consider alternative, transformative business models, including those models that focus on simplicity and leveraging of digital resources. We present some examples of innovative approaches by which some investigators have successfully conducted large-scale, clinical trials at relatively low cost. These examples include randomized registry trials, cluster-randomized trials, adaptive trials, and trials that are fully embedded within digital clinical care or administrative platforms.

Link: https://doi.org/10.1038/nrcardio.2017.60.

Pammolli, Fabio, Lorenzo Righetto, Sergio Abrignani, Luca Pani, Pier Giuseppe Pelicci, and Emanuele Rabosio. 2020. “The Endless Frontier? The Recent Increase of R&D Productivity in Pharmaceuticals.” Journal of Translational Medicine 18 (1): 162. https://doi.org/10.1186/s12967-020-02313-z.

Abstract: Background: Studies on the early 2000s documented increasing attrition rates and duration of clinical trials, leading to a representation of a “productivity crisis” in pharmaceutical research and development (R&D). In this paper, we produce a new set of analyses for the last decade and report a recent increase of R&D productivity within the industry. Methods: We use an extensive data set on the development history of more than 50,000 projects between 1990 and 2017, which we integrate with data on sales, patents, and anagraphical information on each institution involved. We devise an indicator to quantify the novelty of each project, based on its set of mechanisms of action. Results: First, we investigate how R&D projects are allocated across therapeutic areas and find a polarization towards high uncertainty/high potential reward indications, with a strong focus on oncology. Second, we find that attrition rates have been decreasing at all stages of clinical research in recent years. In parallel, for each phase, we observe a significant reduction of time required to identify projects to be discontinued. Moreover, our analysis shows that more recent successful R&D projects are increasingly based on novel mechanisms of action and target novel indications, which are characterized by relatively small patient populations. Third, we find that the number of R&D projects on advanced therapies is also growing. Finally, we investigate the relative contribution to productivity variations of different types of institutions along the drug development process, with a specific focus on the distinction between the roles of Originators and Developers of R&D projects. We document that in the last decade Originator–Developer collaborations in which biotech companies act as Developers have been growing in importance. Moreover, we show that biotechnology companies have reached levels of productivity in project development that are equivalent to those of large pharmaceutical companies. Conclusions: Our study reports on the state of R&D productivity in the bio-pharmaceutical industry, finding several signals of an improving performance, with R&D projects becoming more targeted and novel in terms of indications and mechanisms of action.

Link: https://doi.org/10.1186/s12967-020-02313-z.

Paul, Steven M., Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C. Persinger, Bernard H. Munos, Stacy R. Lindborg, and Aaron L. Schacht. 2010. “How to Improve R&D Productivity: The Pharmaceutical Industry’s Grand Challenge.” Nature Reviews Drug Discovery 9 (3): 203–14. https://doi.org/10.1038/nrd3078.

Abstract: The pharmaceutical industry is under growing pressure from a range of environmental issues, including major losses of revenue owing to patent expirations, increasingly cost-constrained healthcare systems and more demanding regulatory requirements. In our view, the key to tackling the challenges such issues pose to both the future viability of the pharmaceutical industry and advances in healthcare is to substantially increase the number and quality of innovative, cost-effective new medicines, without incurring unsustainable R&D costs. However, it is widely acknowledged that trends in industry R&D productivity have been moving in the opposite direction for a number of years. Here, we present a detailed analysis based on comprehensive, recent, industry-wide data to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity. We then propose specific strategies that could have the most substantial impact in improving R&D productivity.

Link: https://doi.org/10.1038/nrd3078.

Pregelj, Lisette, Martie-Louise Verreynne, and Damian Hine. “Changes in Clinical Trial Length.” Nature Reviews Drug Discovery 14, no. 5 (May 2015): 307–8. https://doi.org/10.1038/nrd4611.

Abstract: Not available. Link: https://doi.org/10.1038/nrd4611.

Reichert, Janice M. “Trends in Development and Approval Times for New Therapeutics in the United States.” Nature Reviews Drug Discovery 2, no. 9 (September 2003): 695–702. https://doi.org/10.1038/nrd1178.

Abstract: The process of clinical development and regulatory review of new therapeutics in the United States was significantly changed by a number of legislative acts passed in the 1980s and 1990s. These acts were designed to encourage the development of innovative products, especially for rare, serious or life-threatening diseases, and to ensure that patients had timely access to these treatments. To assess the effects of the various modifications to the process, the Tufts Center for the Study of Drug Development analysed clinical development and approval data for 554 therapeutics (504 small molecules, 40 recombinant proteins and 10 monoclonal antibodies) approved in the United States from 1980–2001. Trends in the number of approved products and the clinical development and approval times indicated that the effects of these changes were generally beneficial as of the mid- to late-1990s, but that the gains have not been sustained in the early 2000s. Current efforts by the FDA, and the pharmaceutical and biopharmaceutical industry, to reverse the recent tendency toward fewer new approvals and longer approval times are discussed.

Link: https://doi.org/10.1038/nrd1178.

Schuhmacher, Alexander, Oliver Gassmann, and Markus Hinder. 2016. “Changing R&D Models in Research-Based Pharmaceutical Companies.” Journal of Translational Medicine 14 (1): 105. https://doi.org/10.1186/s12967-016-0838-4.

Abstract: Abstract: New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2–32.3 billion (2006–2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.

Link: https://doi.org/10.1186/s12967-016-0838-4.

Smietana, Katarzyna, Marcin Siatkowski, and Martin Møller. “Trends in Clinical Success Rates.” Nature Reviews Drug Discovery 15, no. 6 (June 2016): 379–80. https://doi.org/10.1038/nrd.2016.85.

Abstract: The topic of R&D productivity in the pharmaceutical industry has been discussed for more than 20 years. It has been largely a story of decline. In fact, around 90% of potential drugs that enter Phase I trials are destined to fail, and for more than a decade we have observed a downward trend in clinical success rates at all stages. To update our research, we conducted an outside-in analysis of pharmaceutical development success rates from 1996 until 2014. Using Informa’s Pharma projects database, we tracked the clinical and regulatory phase progression of more than 9,200 novel compounds in development (see Supplementary information S1 (box) for details). Our methodology enables success rates for individual development phases to be determined based on the proportion of successful drugs among all compounds exiting that phase in a given time period. Here, we summarize the key trends we observed. Link: https://doi.org/10.1038/nrd.2016.85.

Sheck, Lorraine, Christopher Cox, Henry T Davis, A Gene Trimble, William M Wardell, and Ronald Hansen. “Success Rates in the United States Drug Development System.” Clinical Pharmacology and Therapeutics 36, no. 5 (November 1984): 574–83. https://doi.org/10.1038/clpt.1984.224.

Abstract: Not available. Link: https://doi.org/10.1038/clpt.1984.224.

Martin, Linda, Melissa Hutchens, and Conrad Hawkins. “Clinical Trial Cycle Times Continue to Increase despite Industry Efforts.” Nature Reviews Drug Discovery 16, no. 3 (March 2017): 157–157. https://doi.org/10.1038/nrd.2017.21.

Abstract: One key issue facing pharmaceutical clinical development organizations has been increasing clinical trial cycle times. Despite substantial effort and attention from the industry on this issue, overall development timelines continue to increase, at both the programme and study levels. Indeed, cycle time continues to be a major area for improvement for drug development, given the current time to market — reported as 13.8 years to go from target identification to first approval in a major market (Pharmaceutical Benchmarking Forum 2016 R&D Performance: Success Rates & Cycle Time, KMR Group, June 2016). Companies that can master the operational challenges and restraints in study design can not only reap rewards of shorter cycle times but can also see first-mover advantages, revenue benefits, longer market protection and improve productivity through reduced expenditure on conducting clinical trials.


Terry, Robert F, Gavin Yamey, Ryoko Miyazaki-Krause, Alexander Gunn, and John C. Reeder. 2018. “Funding Global Health Product R&D: The Portfolio-To-Impact Model (P2I), a New Tool for Modelling the Impact of Different Research Portfolios.” Gates Open Research 2 (July): 24. https://doi.org/10.12688/gatesopenres.12816.2.

Abstract: Background: The Portfolio-To-Impact (P2I) Model is a novel tool, developed to estimate minimum funding needs to accelerate health product development from late stage preclinical study to phase III clinical trials, and to visualize potential product launches over time.

Methods: A mixed methods approach was used. Assumptions on development costs at each phase were based on clinical trial costs from Parexel’s R&D cost sourcebook. These were further refined and validated by interviews, with a wide variety of stakeholders from Product Development Partnerships, biopharmaceutical and diagnostic companies, and major funders of global health R&D.

Results: the tool was used to create scenarios describing the impact, in terms of products developed, of different product portfolios with funding ranging from $1 million per annum through to $500 million per annum. These scenarios for a new global financing mechanism have been previously presented in a report setting out the potential for a new fund for research and development which would assist in accelerating product development for the diseases of poverty.

Conclusion: The P2I tool does enable a user to model different scenarios in terms of cost and number of health products launched when applied to a portfolio of health products. The model is published as open access accompanied with a user guide. The design allows it to be adapted and used for other health R&D portfolio analysis as described in an accompanying publication focussing on the pipeline for neglected diseases in 2017. We aim to continually refine and improve the model and we ask users to provide us with their own inputs that can help us update key parameters and assumptions. We hope to catalyse users to adapt the model in ways that can increase its value, accuracy, and applications.

Link: https://doi.org/10.12688/gatesopenres.12816.2.

Young, Ruth, Tewodros Bekele, Alexander Gunn, Nick Chapman, Vipul Chowdhary, Kelsey Corrigan, Lindsay Dahora, et al. 2018. “Developing New Health Technologies for Neglected Diseases: A Pipeline Portfolio Review and Cost Model.” Gates Open Research 2 (August): 23. https://doi.org/10.12688/gatesopenres.12817.2.

Abstract: Background: Funding for neglected disease product development fell from 2009-2015, other than a brief injection of Ebola funding. One impediment to mobilizing resources is a lack of information on product candidates, the estimated costs to move them through the pipeline, and the likelihood of specific launches. This study aimed to help fill these information gaps. Methods: We conducted a pipeline portfolio review to identify current candidates for 35 neglected diseases. Using an adapted version of the Portfolio to Impact financial modelling tool, we estimated the costs to move these candidates through the pipeline over the next decade and the likely launches. Since the current pipeline is unlikely to yield several critical products, we estimated the costs to develop a set of priority “missing” products. Results: We found 685 neglected disease product candidates as of August 31, 2017; 538 candidates met inclusion criteria for input into the model. It would cost about $16.3 billion (range $13.4-19.8B) to move these candidates through the pipeline, with three-quarters of the costs incurred in the first 5 years, resulting in about 128 (89-160) expected product launches. Based on the current pipeline, there would be few launches of complex new chemical entities; launches of highly efficacious HIV, tuberculosis, or malaria vaccines would be unlikely. Estimated additional costs to launch one of each of 18 key missing products are $13.6B assuming lowest product complexity or $21.8B assuming highest complexity ($8.1B-36.6B). Over the next 5 years, total estimated costs to move current candidates through the pipeline and develop these 18 missing products would be around $4.5B (low complexity missing products) or $5.8B/year (high complexity missing products). Conclusions: Since current annual global spending on product development is about $3B, this study suggests the annual funding gap over the next 5 years is at least $1.5-2.8B. Link: https://doi.org/10.12688/gatesopenres.12817.2.

Thomas, David W., Justin Burns, John Audette, Adam Carroll, Corey Dow-Hygelund, and Michael Hay. "Clinical Development Success Rates 2006-2015" Biomedtracker Report, 2016. 1-28.

Abstract: This is the largest study of clinical drug development success rates to date. Over the last decade, 2006-2015, a total of 9,985 clinical and regulatory phase transitions were recorded and analyzed from 7,455 development programs, across 1,103 companies in the Biomedtracker database. Phase transitions occur when a drug candidate advances into the next phase of development or is suspended by the sponsor. By calculating the number of programs progressing to the next phase vs. the total number progressing and suspended, we assessed the success rate at each of the four phases of development: Phase I, II, III, and regulatory filing. Having phase-by-phase data in hand, we then compared groups of diseases, drug modalities and other attributes to generate the most comprehensive analysis yet of biopharmaceutical R&D success. This work was made possible due to the years of clinical program monitoring and data entry by Informa’s Biomedtracker service. BIO has long partnered with Biomedtracker to calculate success rates based on this data. More recently, BIO and Biomedtracker partnered with Amplion, the inventors of BiomarkerBase, to analyze the effects of biomarkers in clinical trial success.

Link: https://www.bio.org/sites/default/files/legacy/bioorg/docs/Clinical%20Development%20Success%20Rates%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf

Wong, Chi Heem, Kien Wei Siah, and Andrew W Lo. “Estimation of Clinical Trial Success Rates and Related Parameters.” Biostatistics 20, no. 2 (April 1, 2019): 273–86. https://doi.org/10.1093/biostatistics/kxx069.

Abstract: Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

Link: https://doi.org/10.1093/biostatistics/kxx069.

Gunn, Alexander, Shashika Bandara, Gavin Yamey, Flavia D´Alessio, Hilde Depraetere, Sophie Houard, Nicola Viebig, and Stefan Jungbluth. 2019. “Pipeline Analysis of a Vaccine Candidate Portfolio for Diseases of Poverty Using the Portfolio-To-Impact Modelling Tool.” F1000Research 8 (July): 1066. https://doi.org/10.12688/f1000research.19810.1.

Abstract: Background: The Portfolio-To-Impact (P2I) P2I model is a recently developed product portfolio tool that enables users to estimate the funding needs to move a portfolio of candidate health products, such as vaccines and drugs, along the product development path from late stage preclinical to phase III clinical trials, as well as potential product launches over time. In this study we describe the use of this tool for analysing the vaccine portfolio of the European Vaccine Initiative (EVI). This portfolio includes vaccine candidates for various diseases of poverty and emerging infectious diseases at different stages of development. Methods: Portfolio analyses were conducted using the existing assumptions integrated in the P2I tool, as well as modified assumptions for costs, cycle times, and probabilities of success based on EVI’s own internal data related to vaccine development. Results: According to the P2I tool, the total estimated cost to move the 18 candidates currently in the EVI portfolio along the pipeline to launch would be about US $470 million, and there would be 0.69 cumulative expected launches during the period 2019-2031. Running of the model using EVI-internal parameters resulted in a significant increase in the expected product launches. Conclusions: The P2I tool's underlying assumptions could not be tested in our study due to lack of data available. Nevertheless, we expect that the accelerated clinical testing of vaccines (and drugs) based on the use of controlled human infection models that are increasingly available, as well as the accelerated approval by regulatory authorities that exists for example for serious conditions, will speed up product development and result in significant cost reduction. Project findings as well as potential future modifications of the P2I tool are discussed with the aim to improve the underlying methodology of the P2I model.

Link: https://doi.org/10.12688/f1000research.19810.1.

Burrows, Jeremy N., Stephan Duparc, Winston E. Gutteridge, Rob Hooft van Huijsduijnen, Wiweka Kaszubska, Fiona Macintyre, Sébastien Mazzuri, Jörg J. Möhrle, and Timothy N. C. Wells. 2017. “New Developments in Anti-Malarial Target Candidate and Product Profiles.” Malaria Journal 16 (1): 26. https://doi.org/10.1186/s12936-016-1675-x.

A decade of discovery and development of new anti-malarial medicines has led to a renewed focus on malaria elimination and eradication. Changes in the way new anti-malarial drugs are discovered and developed have led to a dramatic increase in the number and diversity of new molecules presently in pre-clinical and early clinical development. The twin challenges faced can be summarized by multi-drug resistant malaria from the Greater Mekong Sub-region, and the need to provide simplified medicines. This review lists changes in anti-malarial target candidate and target product profiles over the last 4 years. As well as new medicines to treat disease and prevent transmission, there has been increased focus on the longer term goal of finding new medicines for chemoprotection, potentially with long-acting molecules, or parenteral formulations. Other gaps in the malaria armamentarium, such as drugs to treat severe malaria and endectocides (that kill mosquitoes which feed on people who have taken the drug), are defined here. Ultimately the elimination of malaria requires medicines that are safe and well-tolerated to be used in vulnerable populations: in pregnancy, especially the first trimester, and in those suffering from malnutrition or co-infection with other pathogens. These updates reflect the maturing of an understanding of the key challenges in producing the next generation of medicines to control, eliminate and ultimately eradicate malaria. Link: https://doi.org/10.1186/s12936-016-1675-x.

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