Appl Health Econ Health Policy (2017) 15:113–118 DOI 10.1007/s40258-016-0279-5

SHORT COMMUNICATION

Promoting Antibacterial Drug Development: Select Policies and Challenges Aylin Sertkaya1 • Amber Jessup2 • Hui-Hsing Wong2

Published online: 6 September 2016  Springer International Publishing Switzerland 2016

Abstract Background The development pipeline for antibacterial drugs has not met the demand of hospitals and healthcare providers struggling to cope with increasing problems of antibacterial resistance. Although the challenges associated with antibacterial drug development have been known for some time, previous efforts to address them have not been sufficient. There remains an urgent need for targeted incentives to foster antibacterial drug development while encouraging prudent use. Objective We examine the effects of two types of incentives, a 5-year delay in competition from generics and a lump-sum US$50 million prize payment upon successful US Food and Drug Administration approval, on antibacterial drug company returns.

Parts of this study have been presented at the following four meetings: (1) National Institutes of Health-US Food and Drug Administration. The Development of New Antibacterial Products: Charting a Course for the Future Workshop [Invited Presentation]. Bethesda, MD, USA; 30 July 2014. (2) Society of Risk Analysis Annual Meeting; Denver, CO, USA; 10 December 2014. (3) Society of Risk Analysis Annual Meeting; Baltimore, MD, USA; 10 December 2013. (4) Brookings Council on Antibacterial Drug Development. Incentives for Change: Addressing the Challenges in Antibacterial Drug Development Workshop [Invited Presentation]; Washington, DC; 27 February 2013. & Aylin Sertkaya [email protected] 1

Eastern Research Group, Inc., 110 Hartwell Avenue, Lexington, MA 02421, USA

2

US Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Office of Science and Data Policy, Washington, DC, USA

Methods We use the decision-tree framework developed in a study for the US Department of Health and Human Services, which models the drug company’s decision process as a revenue maximizer under uncertainty. Results Our results show that, to maximize societal benefit, such incentives need to take into consideration the indication(s) the new antibacterial drug is designed to treat as well as the drug development stage. Conclusions Optimal policies should maximize the difference between societal benefit, primarily measured as the reduction in public health burden from the development of a new antibacterial drug that treats an infectious disease while ensuring prudent use, and social cost. Here, we show that the two types of policies examined under-incentivize early-stage developers (i.e., do not achieve the desired outcome) and over-incentivize late-stage developers (i.e., achieve the desired outcome but at a cost that is higher than needed) ceteris paribus.

Key Points For Decision Makers Designing optimal policies to incentivize antibacterial drug development is not straightforward. Uniform application of policy instruments run the risk of under-incentivizing early-stage companies while over-incentivizing those in later stages of drug development. Policies need to be fine-tuned to achieve the desired result of encouraging antibacterial drug development while maximizing societal benefit.

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1 Background

development (R&D) is US$100 million, the study [9] concluded that:

Antibacterial drugs have transformed our ability to combat deadly infections and saved innumerable lives since their introduction in the 1940s. However, threats of antibacterial resistance have dramatically increased in recent decades and continue to grow. According to the most recent statistics from the US Centers for Disease Control and Prevention, at least 2 million people acquire serious infections with bacteria that are resistant to one or more of antibacterial drugs designed to treat those infections in USA alone. Of these, approximately 23,000 die as a result of drug-resistant infections. Even though estimates vary widely, the economic cost of antibacterial resistance in USA could be as high as US$20 billion and US$35 billion a year in excess direct healthcare costs and lost productivity costs, respectively [1]. Without the development of new drugs, this expansion of resistance will continue to reduce the effectiveness of currently available antibacterial drugs and leave many patients with few, if any, treatment options. Despite the potential of new products to reduce the social burden associated with resistant infections, the pipeline for new antibacterial drugs remains limited. At present, there are only 37 new antibacterial drugs in development for the US market ‘‘designed to treat serious infections that act systemically, or throughout the body’’ [2]. Further, large pharmaceutical companies have been exiting the market for antibacterial drugs driven by insufficient returns to capital. Returns have been poor for a number of reasons, including limited markets for some drugs, short-treatment durations, and challenging clinical trials [3–5]. Others also contend that the US reimbursement system for these drugs is inadequate, compounding the problem of insufficient returns [6, 7]. Increasing antibacterial resistance coupled with an inadequate pipeline of new drugs is raising alarm in the medical community because we may not be able to effectively treat infections in the future [4, 8]. This alarm is stimulating policy debate about how to spur antibacterial drug development while keeping resistance in check through stewardship measures. Even though there is consensus on the need to do ‘‘something,’’ there seems to be disparate views on exactly how to do it. Therefore, the path for policymakers remains unclear. A recent study conducted by Eastern Research Group, Inc., under contract to the US Department of Health and Human Services, i.e., the Sertkaya et al. study, examined the impacts of different incentives on the returns to new antibacterial product development using a decision-tree framework [9]. Assuming that the minimum expected return needed to induce a developer to begin research and

• •

•

•

Delaying entry of generics is not sufficient by itself. The reduction in the cost of capital needed through tax incentives could be as high as 80 %, depending on the type of indication the antibacterial drug is designed to treat. Modifications to the clinical trial process and approval standards need to reduce the time to market by as much as 80 %, depending on the type of indication the antibacterial drug is designed to treat. Grant/award/prize amounts need to increase substantially if paid out at later stages of clinical development.

Building on the Sertkaya et al. [9] study, here, we focus on one part of the problem; our ability to expand the antibacterial drug pipeline by establishing incentives to improve the returns to innovator companies. We demonstrate the challenges of setting optimal incentive levels that would maximize benefits to society even within this narrow focus.

2 Methods We adopt the analytical framework provided in Sertkaya et al. [9, 10], in which a drug company’s evaluation of drug development costs against potential returns is modeled in the form of a decision tree (Fig. 1). In Fig. 1, the net present value (NPV) that the company would earn at each of the end nodes (i.e., red triangles) is given by: NPV ¼

T X ðRi  Ci Þ i¼0

ð1 þ r Þi

ð1Þ

where r is the real opportunity cost of capital that captures the time value effect; T is the total product life including development time; i represents the drug development stage (e.g., pre-clinical, phase I, phase II); and R and C are the revenues and costs at each stage, respectively. Given that the uppermost branch represents the case where the drug completes all development stages and successfully reaches the market, it is the only scenario where the company earns a positive NPV. By contrast, if the company pushed forward with development but the drug failed at some point, it would incur the costs of the clinical trials and other supply chain-related activities without earning any revenues. Therefore, all other outcome nodes in the figure represent negative NPVs. The marginal returns at each chance node (i.e., green circles) are calculated from right to left across the tree by multiplying the NPVs associated with each outcome by the probabilities of that outcome occurring. These values thus

Antibacterial Drug Policy Challenges Fig. 1 Decision tree depicting expected net present value (ENPV) of developing a new hypothetical antibacterial drug by stage of development [9]

115 PRECLINICAL

PHASE I

t1

PHASE II

t2

t3

PHASE III

NDA/BLA

t4

Success

p4 Success

p3 Success

p2 p1

Success

NPV5

E(NPV5,4)

1-p5 E(NPV54,3)

Failure

NPV4

1-p4 E(NPV543,2)

Failure

NPV3

1-p3

Success

Develop

t5

p5

E(NPV5432,1)

NPV2

Failure

1-p2 E(NPV54321,0)

NPV1

Failure

1-p1 NPV0

Failure Abandon

$0

Notes: NPV = Net Present Value NDA/BLA = New Drug Applicaon or Biologic License Applicaon ti = Duraon of phase i where i = 1,…, 5 pi = Probability of success in phase i where i = 1,…, 5 1 – pi = Probability of failure in phase i where i = 1,…, 5

Fig. 2 Expected net present value (in US$ million) by stage of development and indication the new drug is designed to treat [9]

HABP/VABP

CUTI

CIAI

CABP

ABSSSI

ABOM -US$1,000

US$0

US$1,000

US$2,000

US$3,000

US$4,000

ABOM NDA/BLA Submission US$1,355.4

ABSSSI US$3,183.6

CABP US$3,870.3

CIAI US$2,223.2

CUTI US$3,158.7

HABP/VABP US$1,631.1

Phase 3

US$685.0

US$1,875.2

US$2,284.5

US$1,148.5

US$1,663.7

US$695.5

Phase 2

US$282.3

US$815.6

US$999.7

US$490.3

US$722.8

US$250.6

Phase 1

US$73.2

US$231.6

US$286.3

US$135.1

US$204.2

US$64.0

Pre-clinical

-US$2.7

US$27.1

US$37.4

US$8.9

US$21.9

-US$4.5

Notes: ABOM = Acute bacterial os media ABSSSI = Acute bacterial skin and skin structure infecons CABP = Community acquired bacterial pneumonia CIAI = Complicated intra-abdominal infecons CUTI = Complicated urinary tract infecons HABP/VABP = Hospital acquired/venlator associated bacterial pneumonia NDA/BLA = New Drug Applicaon or Biologic License Applicaon

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represent the expected NPVs (ENPVs). For example, the ENPV at the start of the New Drug Application or Biologic License Application review phase, E(NPV5,4), in Fig. 1 is computed as:   E NPV5;4 ¼ p5  NPV5 þ ð1  p5 Þ  NPV4 ð2Þ where p and (1 - p) are the success and failure probabilities, respectively. This value, E(NPV5,4), can then be used to do the same calculation for the chance node at phase III, and so forth until the value at the first chance node can be calculated. The value, E(NPV54321,0), represents the ENPV to the company of moving forward with drug development at the time when the decision is made to continue or abandon the new compound. It includes all US revenues, total cost of pre-clinical and clinical research, and supply chain activity-related costs for the drug that successfully makes it to market as well as those that fail at some stage. At each node, the company can re-evaluate the ENPV and make the decision whether to continue with development. Innovator company revenues and costs vary by the type of disease(s) a new antibacterial drug is designed to treat. Thus, we estimate the ENPV at each node along the decision tree of developing new antibacterial drugs indicated for the treatment of six bacterial diseases for which the model parameter values are readily available from Sertkaya et al. [9]: • • • • • •

Acute bacterial otitis media; Acute bacterial skin and skin structure infections (ABSSSI); Community-acquired bacterial pneumonia; Complicated intra-abdominal infections; Complicated urinary tract infections; and Hospital acquired/ventilator-associated bacterial pneumonia (HABP/VABP).

Using this decision-tree framework, we can examine the impact of two different types of incentives designed to increase the ENPV to the innovator company developing a new antibacterial drug: • •

1

5-year delay in competition from generics,1 and Lump-sum US$50 million prize payment upon successful US Food and Drug Administration (FDA) approval of the new antibacterial drug.2

The modeled incentive corresponds to Section 805 of the Generating Antibiotic Incentives Now Act, signed into law as part of the Food and Drug Administration Safety and Innovation Act, which extends the Hatch Waxman provisions related to data exclusivity by 5 years for a Qualified Infectious Disease Product [11]. 2 On 18 September 2014, the White House issued a series of new actions designed to incentivize efforts to combat antibiotic-resistant bacteria. One of these actions included the launch of a US$20 million prize ‘‘… sponsored by the National Institutes of Health (NIH) and the Biomedical Advanced Research and Development Authority

If an innovator is able to charge a price premium for its drug owing to a lack of competition from therapeutically similar drugs, a delay in generic competition can increase revenues and hence the ENPV by allowing the innovator companies to charge consumers and insurers higher prices for their drugs over a longer period of time. FDA-conferred exclusivities, patent term adjustments, patent term extensions, and supplementary protection certificates3 granted to innovator companies are the legal mechanisms through which generic entry into a market can be delayed. Prizes directly reduce R&D costs or increase revenues and can take a variety of forms, including milestone monetary prizes, best entry tournaments, elective systems (e.g., the optional reward scheme), and others. Similar to the impact of a delay in generic competition, receipt of a lump-sum prize of US$50 million upon FDA approval also increases the ENPV of the innovator company.

3 Results Figure 2 presents the estimated ENPVs for each of the six new antibacterial drugs, by development stage and indication the drug is designed to treat. It should be noted that the model parameter values adopted from Sertkaya et al. [9] do not represent any specific antibacterial drug but are broadly representative of antibacterial development costs and returns for each of the above indications. Once a company successfully moves from one stage of development to another along the spectrum (e.g., from preclinical R&D to phase 1 and then from phase I to phase II), the costs incurred in previous stages become ‘‘sunk’’ in economic terms and no longer enter into the decision process of the forward-looking agent. Consequently, the ENPV increases as the company moves to later-stage decision nodes. Figure 3 shows the estimated value of each of these incentives, expressed in terms of change in ENPV, to innovator companies by stage of drug development as well as the indication the new antibacterial is designed to treat. As can be gleaned from Fig. 3, the value of both types of incentives is much greater for an innovator company that is Footnote 2 continued (BARDA) to facilitate the development of a rapid diagnostic test to be used by health care providers to identify highly resistant bacterial infections at the point of patient care’’ [12]. The choice of US$50 million for the case study presented in the paper was loosely based on this US$20 million amount. We assumed that for antibacterial drugs, a lump-sum prize that is 2.5 times that for a rapid diagnostic test might be more appropriate. 3 A supplementary protection certificate is a type of incentive used in the European Union as well as Iceland, Liechtenstein, and Norway, which extends the patent life for specific pharmaceutical and plant protection products when granted.

Antibacterial Drug Policy Challenges Fig. 3 Value of incentives in terms of change in expected net present value (in US$ million) to the innovator company by stage of development and indication the new antibacterial is designed to treat

117 Pre-clinical

Phase 1

Phase 2

Phase 3

US$350.0

US$300.0

US$250.0 Value of a 5-year Delay in Generic Entry

US$200.0

US$150.0

US$100.0

US$50.0

US$0.0

US$50.0 US$0.0 ABOM

ABSSSI

CABP

CIAI

CUTI

HABP/VABP

Lump-sum Prize of US$50 Million Upon FDA Approval

Notes: ABOM = Acute bacterial os media ABSSSI = Acute bacterial skin and skin structure infecons CABP = Community acquired bacterial pneumonia CIAI = Complicated intra-abdominal infecons CUTI = Complicated urinary tract infecons HABP/VABP = Hospital acquired/venlator associated bacterial pneumonia

at a later stage in drug development than for one that might be at the start of the pre-clinical R&D phase. For example, for an innovator company of an antibacterial drug designed to treat community-acquired bacterial pneumonia, the estimated increase in ENPV from a 5-year delay in generic entry is US$9 million prior to the pre-clinical R&D stage and US$346 million at the start of phase III. Similarly, for the same innovator company, the value of a US$50 million lump-sum prize upon FDA approval is US$0.8 million prior to the pre-clinical R&D stage and US$30.2 million at the start of phase III after having successfully completed phase II. The value of the incentive to the innovator company also varies by the type of indication the new drug is designed to treat. For example, while a 5-year delay in generic entry to a company that is at the start of phase I trials of a drug indicated for HABP/VABP is US$14.7 million, it is US$38.3 million for another company in the same stage of development for a drug indicated for ABSSSI. Likewise, the value of a US$50 million prize is US$3.0 million and US$4.0 million for a company at the start of phase I trials of a drug indicated for HABP/VABP and for another company in the same stage of development for a drug indicated for ABSSSI, respectively.

A 5-year delay in generic entry results in a transfer payment from the users of the drug to the innovator company, as they would be paying premium prices for the brand drug for a period longer than they would have been in the absence of the incentive. In contrast, the lump-sum prize is a transfer payment from all tax payers, not just the users of the drug, assuming the prize is federally funded.

4 Conclusions Overall, the values of both incentives progressively increase for innovator companies as they get closer to a product launch. This is primarily owing to the fact that: (1) companies in earlier stages have more years over which the cost of capital is compounded until the drug reaches the market; (2) costs associated with prior completed stages become sunk and do not enter into the decision process of the forward-looking company as it moves along the drug development spectrum; and (3) the probability of the drug successfully reaching the market increases. From a societal perspective, the incentive levels need to be sufficient to persuade the companies to develop antibacterial drugs but must also be balanced against

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limited societal resources for healthcare. The goal is to find an incentive level that maximizes the difference between the societal benefit (primarily measured as the reduction in public health burden from the development of a new antibacterial drug that treats an infectious disease while ensuring prudent use) and the social cost. The two types of incentives examined under-incentivize early-stage developers (i.e., do not achieve the desired outcome) and overincentivize late-stage developers (i.e., achieve the desired outcome but at a cost that is higher than needed) ceteris paribus. Hence, the examples demonstrate the difficulty of evaluating optimal incentive levels needed to spur the development of new antibacterial drugs given the industry landscape where different companies are at different stages of development and are likely pursuing the development of drugs targeting different bacterial infections. As shown, the incentive levels need to be much greater to entice companies to enter the antibacterial drug market during the early stages of drug development than to encourage those that are already in clinical trials to continue. Blunt policy instruments applied uniformly run the risk of under-incentivizing these potential early-stage entrants while over-incentivizing others that might be close to a successful product launch. Model uncertainty as noted in Sertkaya et al. [9] also adds to the difficulty of ascertaining optimal incentive levels. Thus, further research is needed to identify ways to fine-tune different policy instruments and to refine model parameters. Acknowledgments The authors gratefully acknowledge Edward Cox (US Food and Drug Administration), Peter Lurie (US Food and Drug Administration), Michael Lanthier (US Food and Drug Administration), and Kevin Outterson (Boston University) for their insightful comments, advice, and guidance. The authors also would like to thank Anna Birkenbach (Eastern Research Group, Inc.), Nyssa Ackerley (Eastern Research Group, Inc.), and Calvin Franz (Eastern Research Group, Inc.) who provided invaluable research support. The views expressed in this paper are those of the authors and do not necessarily represent those of the US Food and Drug Administration, Office of the Assistant Secretary for Planning and Evaluation, the US Department of Health and Human Services, or Eastern Research Group, Inc. Author Contributions Aylin Sertkaya developed the decision-tree framework, conducted the quantitative analysis presented, and wrote the background and results sections of the paper. Hui-Hsing and Amber Jessup developed the original research question, identified the incentives to be examined, and wrote parts of the methods and discussion sections, respectively. Each author also served as a reviewer of other authors’ sections.

Compliance with ethical standards Funding The funding for this study was provided by the US Department of Health and Human Services Office of the Assistant

A. Sertkaya et al. Secretary for Planning and Evaluation and the US Food and Drug Administration under Contract No. HHSP23320095634WC Task Order No. HHSP23337004T. Conflict of interest Aylin Sertkaya, Amber Jessup, and Hui-Hsing Wong have no conflicts of interest to declare for this research.

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