Low hanging fruit in infectious disease drug development

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Cost estimates for developing new molecular entities (NME) are reaching non-sustainable levels and coupled with increasing regulatory requirements and oversight have led many pharmaceutical sponsors to divest their anti-microbial development portfolios [Projan SJ: Why is big Pharma getting out of anti-bacterial drug discovery?Curr Opin Microbiol 2003, 6:427–430] [Spellberg B, Powers JH, Brass EP, Miller LG, Edwards JE, Jr: Trends in antimicrobial drug development: implications for the future.Clin Infect Dis 2004, 38:1279–1286]. Operational issues such as study planning and execution are significant contributors to the overall cost of drug development that can benefit from the leveraging of pre-randomization data in an evidence-based approach to protocol development, site selection and patient recruitment. For non-NME products there is even greater benefit from available data resources since these data may permit smaller and shorter study programs. There are now many available open source intelligence (OSINT) resources that are being integrated into drug development programs, permitting an evidence-based or ‘operational epidemiology’ approach to study planning and execution.

Introduction

Two decades after the advent of the antibiotic era there was a prevailing belief that infectious diseases were soon to be part of a closing chapter in the history of human medicine. It is not clear from where the quote originated, but the statement that “It is time to close the book on infectious diseases, and declare the war against pestilence won [3] seems to have captured the zeitgeist. Six decades have now passed since the first randomized controlled trial in infectious diseases was initiated [4], and the chapter on infectious diseases has not closed. Rather, infectious diseases remain an ever-present and changing threat to human health, considered the second-leading cause of death globally, directly resulting in approximately fifteen million deaths annually [5].

At least four crucial factors have contributed to the persistent demand for innovative anti-infective products in the global marketplace: (1) consistent development of drug resistance by targeted pathogens, (2) the re-emergence of pathogens previously considered controlled or of declining significance, (3) the identification of new pathogens, and (4) the very real threat of bioterrorism [6, 7, 8, 9, 10]. These factors have resulted in the growth of the global infectious diseases marketplace (anti-bacterials, anti-virals, anti-fungals, and vaccines) to an estimated $48.5 billion in annual sales with projections to exceed $60.6 billion by 2011 [11]. Challenging the growth of this market are obstacles intrinsic to this clinical area [1], including: indications that require short courses of therapy, a marketplace that is highly competitive, and competition from generic drugs that is shrinking revenues for innovator companies [2].

Compounding the specific challenges of anti-infective drug development are the industry-wide difficulties of rising costs and shrinking pipelines. Estimated development costs for a NME are now exceeding $1 billion, pharmaceutical marketplace shareholder value loss is estimated to be in excess of $850 billion and only three of ten drugs that reach the marketplace recouping their respective development costs [12, 13, 14, 15].

There are multiple reasons for the increasing costs, both in time and money, for drug development programs. Most salient is the increased requirement of study participants [12, 16] but other factors have also become more prominent in the past decade, including increased safety requirements, fewer first round approvals, increasing size of submitted new drug applications, increasing complexity of studies, increasing number of advisory committee reviews, declining investigator participation rates, and declining patient participation rates [17, 18, 19]. The ultimate results are costlier trials with longer development timelines [20]. The specific shortfalls in anti-infective drug development have led to a concerted response from the infectious disease community, calling for a change in the business model so that issues such as lower profitability do not hinder the development of life-saving therapies in an era of increasing drug resistance and sponsor divestment [2, 9, 21, 22•].

Section snippets

Stakeholder responses to drug development challenges

There are a number of responses to these shortfalls from concerned professionals in academia, government, and industry. There has been a clarion call from the infectious disease community asking stakeholders to address the growing crisis in antibiotic drug development, with recommendations directed at Congress, the United States Food and Drug Administration, and the National Institute of Allergy and Infectious Diseases [23]. Some changes have indeed occurred. There are new sources of funding, a

Secondary use of health care data in drug development

A clinical trial that is delayed as a consequence of new amendments, is unable to recruit patients, cannot secure the interest from targeted investigators, or has an inappropriate design to meet regulatory requirements can not only result in a large financial burden but can also result in the need for more trials or simply registrational rejection. Opportunities do however exist for identifying and correcting such errors earlier in the development lifecycle and for limiting the likelihood of

Regulatory precedent

Given that more than 50% of New Drug Application (NDA) submissions to the US FDA have received marketing approval from another regulatory agency there is more often than not relevant pre-randomization data that can add substantial value to a US FDA submission [35]. There are multiple examples among recent anti-infective development programs where standard requirements for large trials and development timelines have been curtailed through the use of such data. Tinidazole (Tindamax, Presutti

Study planning

The use of non-confidential data available in the public domain either free or for purchase for the purpose of decision-support in drug development has recently been described as ‘Open Source Intelligence (OSINT)’ [40••]. Such data sources include information related to genomic targets, disease epidemiology, trial design and competitive intelligence. When integrated into planning workflow they can have a dramatic impact on cost and timelines since modeling study variables such as, investigator

Study execution

Evidence-based study planning permits the tailoring of inclusion and exclusion criteria, selection of appropriate countries and sites based on disease prevalence data, and allows drug sponsors to forecast patient recruitment estimates, crucial for budget management. In many ways this can be considered the ‘operational epidemiology’ of a clinical trial, characterizing the patient pools, assessing countries with the most favorable political and economic infrastructure for study conduct as well as

Conclusions

Infectious diseases drug development has changed considerably since Amberson used a coin toss to allocate patients in his pulmonary tuberculosis trial [50]. Although we face the obvious challenges of emerging and re-emerging pathogens, drug resistance, and anti-microbial stewardship to prolong the utility of developed compounds, we also face the rising costs of drug development associated with patient recruitment and data management. There are low hanging fruit available to all stakeholders in

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

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