Box S1 | Modelling tools used · general purpose tool to optimize therapeutic clinical study designs with biomarkers. Clinical development simulation model for trastuzumab and panitumumab

SUPPLEMENTARY INFORMATION In format provided by Trusheim et al. (NOVEMBER 2011)

NATURE REVIEWS | DRUG DISCOVERY www.nature.com/reviews/drugdisc

Box S1 | Modelling tools used

Multiple models used to address the innovation chain scope. The variety of issues affecting the stratified medicine innovation chain across many functions over nearly two decades required the team to use a corresponding variety of tools. Even though standard methods were used, expecting a single tool to include all the methods required from clinical trial design and power calculations to discounted cash flow analyses was deemed impractical. In addition, the project wished to compare and contrast differing modeling approaches to the same issues in order to distinguish methodological versus assumption and data drivers for varying results. This multi-tool approach also allowed the working teams to encourage various participants to bring their existing tools to bear on the problem which both accelerated the process and enabled the use of ‘state of the practice’ tools. A disadvantage of this approach was that the team often had to link the tools manually via data input.

Figure S1 illustrates the five distinct modeling tools used in the analyses. They include a clinical trial design tool explicitly incorporating biomarker driven considerations, two clinical development simulation models and two broad economic models. The models spanned the medical product innovation chain from Phase II clinical development through regulatory approval to commercial therapeutic exclusivity expiry.

Figure S1| Models used in the case study analyses.

The tools are summarized in Box 1 of the main text and described in greater detail below. For oncology, the team augmented the PCSD tool with a clinical trial simulator constructed for the project by GlaxoSmithKline (GSK) to estimate the required clinical trial sizes for each development scenario based on the available historical clinical development data for trastuzumab (Herceptin; Genentech) and panitumumab (Vectibix; Amgen). This clinical trial information, combined with more than 75 other parameters estimated based on public data sources, expert interviews and team expert experience, was then used by economic models developed at MIT and IMS Management Consulting, IMS Health.



For Alzheimer’s disease, results from the Phase II trial and information on the subsequent Phase III trial design for bapineuzumab were input into a Monte Carlo clinical trial simulation model, constructed by Bristol-Myers Squibb (BMS) for the project, that calculated the expected probability of technical and regulatory success (PTRS) of alternative development strategies. The resultant study sizes and PTRS values were inserted into the MIT Stratified Medicine Model along with the other required inputs to then generate economic value estimates.

For all three case studies, economic outcomes were measured and compared based on net present value (NPV) and on risk-adjusted, expected net present value (eNPV). Pharmacogenomic Clinical Study Design (PCSD) software tool. Through increasing interaction between industry researchers and the US FDA, it became clear that many researchers faced a common problem with designing a clinical trial that utilizes predictive biomarkers and assessing the feasibility of such a study. As a result, beginning in 2005 the FDA developed a software tool with Adaptive Pharmacogenomics and expanded the team with the involvement of GSK under a Cooperative Research and Development Agreement (CRADA) in 2008. The original tool was developed to use characterized biomarkers to optimize clinical study design, particularly for late Phase II and Phase III studies. GSK developed its tool to assess the utility of specific biomarkers prior to Phase III of clinical development. The existence of the two tools provided an opportunity to develop a single, general software tool that can help to guide the use of stratified medicine throughout clinical development. Such a tool needs to take account of different study designs and analysis strategies (as typically applied in Phases II and III). The FDA, GSK and Adaptive Pharmacogenomics have combined efforts in the CRADA to develop a general purpose tool to optimize therapeutic clinical study designs with biomarkers. Clinical development simulation model for trastuzumab and panitumumab case studies (GSK Model). Team members from GSK developed a clinical trial simulator for the analysis of the oncology case studies. For each study on the development path, it estimates the duration, sample size and Probability of Success (POS) also referred to as the power of the trial design. The duration of the study takes account of the sample size, recruitment rate and the time on treatment. Where the study size is taken from the actual case and is fixed, we estimate the power and where studies are prospectively designed, the sample size is estimated to give 90% power in the study. Both power and sample-size were estimated using simulation using standard methodsS1, S2, S3. The first step in the simulation involved randomly allocating a subject to a treatment and biomarker group. This allocation was based on the relative proportions of each group. The next stage involved the generation of a response for each subject by randomly selecting a value from a distribution. For binary responses, a binomial distribution was used and for time-to-event data, a Weibull distribution was used. The parameters used for these distributions differed for each treatment and biomarker group and were based on published data or credible assumptions. Once the data were simulated, they were analysed using logistical regression for the binary endpoints and by parametric survival analysis. The variables used in the model depended on the study design and included, where appropriate, a term for the treatment effect, biomarker effect and an interaction effect.



One thousand studies were simulated and the probability of success was the proportion of all studies with a successful outcome. The definition of a successful outcome depended on the study design. In single-arm studies, a study with an overall response greater than the clinical target was considered a success. Where these studies failed and a retrospective biomarker evaluation was performed, a success involved the detection of a significant biomarker effect. In comparative studies, a significant treatment effect resulted in a successful study and, where this study failed, a significant biomarker by treatment interaction was considered a success for the corresponding retrospective biomarker analysis. Clinical development simulation model for bapineuzumab (BMS Model). Team members from BMS developed a clinical trial simulator for the analysis of the AD bapineuzumab case study. The model is a Bayesian clinical trial simulator configured to simulate a Phase III trial based on the efficacy results of a Phase II trial accounting for the presence or absence of patient population enrichment from a predictive biomarker whose performance was also observed in the Phase II trial or exogenously. It employs techniques highly similar to those described by Nixon, et al.S4 It estimates the trial size, duration and PTRS of the Phase II trial. The MIT stratified medicine model. The MIT Stratified Medicine Model connects the performance of biomarker for a stratified medicine to the market and financial performance of the commercialized products based on the framework described in 2007 stratified medicine articleS5. It provides an environment in which varying the performance of a biomarker in separating treatment responders from non-responders improves the observed clinical therapeutic effect in treated patients which flows through simple functions (linear, exponential, Gompertz, or look-up table) to affect adoption speed, market shares, and price premiums for those products. The remainder of the model collects additional inputs regarding population sizes, costs, revenues and discount factors for a traditional discounted cash flow model to calculate the net present value of both diagnostic and therapeutic components of the stratified medicine. The model accommodates inputs in the following major categories and detailed in Table S1: • Predictive biomarker/companion diagnostic performance • Therapeutic efficacy variability • Clinical development plan for therapeutic and companion diagnostic, and

therapeutic patent life • Probability of technical and regulatory success. The probability that a candidate

product upon entering a development or regulatory phase will complete that phase.

• Patient benefits and adverse events • Adoption rates, market shares and off-label usage • Market sizes for therapeutic and diagnostic • Pricing and price premiums • Capital and other cash costs • Financial margins, tax rates and discount rate



The model calculates: • Therapeutic efficacy and population enrichment • Companion diagnostic performance (sensitivity, specificity, positive predictive

value, negative predictive value) • Therapeutic and diagnostic approval and launch dates • Clinical adoption speed • Patients tested and treated with the total benefit and adverse event incidents • Price premiums and resultant sales curves • Financial NPVs for both diagnostic and therapeutic

The model is structured in a series of modules. Figure S2 shows the MIT Stratified Medicine Model Dashboard module which condenses the key inputs and results into a single dynamic display. Designed to fill a single screen, the Dashboard contains the vast majority of the inputs and the major outputs including the Adoption Curve, Market Position and Financial Results graphs. The Input Assumptions sheet contains additional, more tabular inputs regarding the therapeutic life cycle, development costs and the assumptions regarding market response to better performing therapies. The model contains additional modules for input assumption documentation, intermediate calculations and scenario storage. The model with the trastuzumab stratified medicine and all-comers cases may be found at the MIT Center for Biomedical Innovation <http://web.mit.edu/cbi/research/Strat_Med.html> website.

MIT Stratified Medicine Model Dashboard: Linking Clinical Development and Biomarker Performance to

Patients and Markets

Figure S2| MIT Stratified Medicine Model Dashboard Sheet. The IMS Health personalized medicine strategy analysis tool. The IMS Health tool is a discounted cash flow model incorporating Monte Carlo simulation decision



analysis techniques designed to help users optimize strategies for stratified medicine development and commercialization. It enables quantification of commercial value for a stratified medicine approach conceptualized in a 2005 publication on value creation and reallocation across healthcare industries in a future world of personalized medicineS6. As illustrated in Figure S3, it has three key features:

Figure S3| Illustration of the IMS Stratified Medicine Drug Development and Commercialization Strategy Decision Analysis Modeling.

1) Strategies are modeled. The user defines multiple strategies (i.e. alternative strategic options) as part of the model inputs. The model then quantitatively evaluates them along multiple financial metrics, including revenue, risk-adjusted net present value (NPV) and probability-linked range of uncertainties.

Strategies are defined as a series of specific choices for each of the key variables relevant to a decision. In the case of stratified medicine development and commercialization strategy modeling, key strategy elements included choice of biomarker, choice of diagnostic co-development partnering, indication and line-of-therapy choice, clinical trial design options and competitor response to companion diagnostic tests.

As part of the model design, the relationships of model variables are depicted in decision trees, which provide the basis for the model calculation

The model has capacities built-in to handle multiple indications and lines-of therapies for each strategic option. Specifically for the Herceptin case, two indications were modeled: a first-line therapy in metastatic breast cancer as the first indication, followed by an adjuvant breast cancer indication approved and launched a few years later.

2) Uncertainties are considered. Three types of uncertainties are considered in the model: probability of technical and regulatory success (PTRS) of each drug and predictive biomarker/companion diagnostic test, other probability of outcomes or decisions (usually tied to relevant nodes on the decision trees) that are not already part of the PTRS, and the value range (and corresponding percentile probability) of each variable.



The model requires that the user determine and enter probability-linked range of values for every input variable. Typically, for a specific variable, the model requests the user to input three representative value points at 25%, 50% and 75% percentile levels for a variable. If necessary, the user could also choose and input value ranges at different percentile levels.

3) Decision outcomes are simulated. Monte Carlo simulations are used to determine possible outcomes for each strategy, along with a corresponding probability for each outcome.

The resulting range of risk-adjusted NPV values of a strategic option is usually specified (and graphed) as a range representing the expected NPV value, 10th and 90th percentile probability bounds (i.e. the lower and upper range).

In addition to NPV outputs, the model produces the “drivers of value” for a strategy (usually reported in a “water-fall” chart), and determines the rank-order impact of the single variable value ranges (usually reported as a “tornado” chart). These results indicate: i) which variables account for the majority of NPV value differences among strategic alternatives, and, ii) for a specific strategic alternative, which variables induce the largest NPV changes.

Overall, the IMS modeling provides information regarding the probability-linked outcomes of strategies, which input variables most impact those strategies either randomly or in response to stakeholder actions. CASE STUDY MODEL DATA AND ASSUMPTIONS The team collected publicly available information for each case study to establish the base case input values for the scenarios. For those inputs for which no public information was available the team generated a consensus estimate based upon the team members’ experience and consulted experts in the field. The authors retain all responsibility for the final values selected and used in the analyses. Information sources were used in the following priority:

1. Regulatory reviewed and published information such as drug and diagnostic product labels or SEC financial filings.

2. Peer reviewed publications specific to the therapeutic or diagnostic 3. Peer reviewed publications for the therapeutic class 4. Publications in non-peer reviewed publications for the product or product

class such as the New York Times 5. Company provided, but not peer reviewed, data such as press releases and

self-reported data such as in ClinicalTrials.Gov 6. Peer reviewed publications regarding the drug or diagnostic industry 7. Team consensus estimates based on experience, internal corporate data and

expert consultation

Base scenarios were validated to the extent possible with public information regarding actual results such as total doses shipped and total product revenues. Scenarios for alternative development strategies or other sensitivities were built on the base cases changing the minimal number of input parameters possible to reflect the alternative and so maintain close comparability to the base case.



Table S1 contains detailed listing of the economic model input variables, their 50th percentile values and sources for the trastuzumab adjuvant therapy base case (biomarker positive development strategy) and the trastuzumab all-comers development strategy alternative. FINANCIAL ASSUMPTIONS USED IN ALL CASE STUDIES In all scenarios product costs of goods sold (COGS), sales, general and administrative expenses (SG&A) and tax rate were established as percentages of revenue grounded in the sponsor’s actual results or large biopharmaceutical company averages. With regard to diagnostic development, the models assume a $9 million cost for 8 months of assay development and a 12 month clinical trial and a $3 million cost for the diagnostic review based on team member experience and consensus due to a paucity of published benchmarks for companion diagnostic development. The large biopharmaceutical reference rate of 9% was used as the discount rate which is somewhat lower than that used by some other researchersS7 but close to the estimate large biopharmaceutical firm weighted average cost of capitalS8, S9. The NPV calculations were not burdened with an ongoing R&D allocation during the product’s commercial period. ONCOLOGY TRASTUZUMAB MODEL DATA AND ASSUMPTIONS Trastuzumab clinical data and assumptions. As described in the main text and Box 1, the trastuzumab case study was used to both analyze the stratified medicine approach and to cross-check the economic models. We examined the trastuzumab FDA approved labelS10 and published studies for trastuzumab both in the metastatic breast cancer settingS11, S12 and in the adjuvant therapy setting for breast cancerS13, S14,

S15. As described in the main text, most modeling focused on the adjuvant therapy indication. The trastuzumab case study begins with the Phase II study and examined development approaches as described in Box 2 plus the additional scenario in which a small Phase II study was conducted without knowledge of the HER2 predictive biomarker. From the actual clinical trial data collected in an enriched stratified environment the trastuzumab performance in an all-comers population was estimated (Table S2). Where response data in the marker negative groups is not available, we assume that the response in this group is similar to the response in a similar population with conventional treatment. The response in the all-comer population is then the weighted average of the response in the biomarker subgroups.

Table S2| Trastuzumab estimated objective response and hazard ratio for an all-comers based on clinical evidence from the stratified setting.



The actual and estimated objective response and hazard ratios were then used to inform the clinical development event trees and clinical simulation performed with the GSK model (Figure S4).The clinical trial sizes were established to provide a 90% probability of detecting a trastuzumab therapeutic effect. As shown in Figure S4, Phase II trial sizes range from 37 in the actually conducted trialS10 to the calculated 582 required patients in an all-comers Phase II trial. In the Phase III trials the patient numbers range from 7,138S10 to the calculated 21,000. The performance (sensitivity, specificity, positive predictive value and negative predictive value) of the HER2+ companion diagnostic in identifying responders was also estimated from the data in the published clinical studiesS10-S15.

Figure S4| Trastuzumab clinical development event tree and simulation results Clinical trial costs were assumed to be as reported in the literature for an all-comers oncology trial plus $1,000 for predictive biomarker testing and further adjusted to reflect trial management costs, regulatory analysis costs per team consensus estimatesS16, S17. Companion diagnostic development costs, regulatory review time and costs were set consistent with those described above for bapineuzumab. Trastuzumab market data and assumptions. The trastuzumab case study examined the global breast cancer market by using worldwide breast cancer incidence but simplified the geographic commercial complexities by assuming a unified global market for reimbursement, patient access and market penetration. Global breast cancer incidence figures were adjusted for the fraction of patients classified as early stage, the eligibility of those for adjuvant therapy and the fraction of patients reachable, compliant and covered by insurance (public or private) based on corporate

Legend 2 Black: Companion Diagnostic Positive Only Approach: Actual trastuzumab development path Blue: All-comers with retrospective analysis: actual all-comers Phase II trial Red: Dual-design approach (multiple objective all-comers and stratified): adequately sized all-comers Phase II trial Green: Adequately sized all-comers development approach



internal market research data. Time to peak sales and market share in eligible patient population for the adjuvant setting were 4.6 years and 63% respectively based on historical industry market research data. A 2% annual growth in treatment accessible patients was also assumed based on corporate internal market research data. The adoption speed, unit sales (and so market share) and pricing of trastuzumab in the stratified medicine setting was taken from Genentech annual reportsS18 and industry data sources such as IMS HealthS19. For the hypothetical all-comers scenario in which trastuzumab would possess substantially reduced observed efficacy in the clinical trials, time to peak sales was lengthened by 1.7 years, peak market share reduced to 10% and price was reduced by ~25% based on expert interviews and team consensus. Table S2 lists the input parameters values, and their sources, used in the MIT and IMS Health economic models for the trastuzumab historical and all-comers validation cases. ONCOLOGY PANITUMUMAB MODEL DATA AND ASSUMPTIONS Panitumumab clinical data and assumptions. The panitumumab case study was selected to explore potential variation in the oncology therapeutic area. Like trastuzumab it has an extensive clinical development and commercial history. As described in the main text, it has utilized two companion diagnostics; one developed prospectively (EGFR) and one retrospectively (KRAS). We examined the panitumumab FDA approved labelS20 and published studies for panitumumabS21-S25. The modeling focused on the more hypothetical case of the value KRAS might have provided proactively rather than an examination of post-approval stratification dynamics. The panitumumab case study begins with a Phase II study and examined development approaches as described in Box 2 with additional scenarios to examine discovering the KRAS predictive biomarker in either Phase II or Phase III retrospective analyses. From the actual clinical trial data collected in an enriched stratified environment the panitumumab performance in both stratified and all-comers populations were estimated (Table S3).

Table S3| Panitumumab estimated objective response and hazard ratio for an all-comers and stratified populations based on evidence from the clinical development record The actual and estimated objective response and hazard ratios were used to inform the clinical development event trees and clinical simulation performed with the GSK model (Figure S5). The clinical trial sizes were established to provide a 90% probability of detecting a panitumumab therapeutic effect. As shown in the Figure



S5, Phase II trial sizes range from the 20 that would be required with fore knowledge of the KRAS marker to the 203 required for a Phase II retrospective analysis. In the Phase III trials the patient numbers range from 150 to 713 in the Phase III all-comers followed by an retrospective analysis. The performance (sensitivity, specificity, ppv and npv) of the KRAS companion diagnostic in identifying responders was also estimated from the data in the published clinical studies. Average industry oncology clinical trial costs per patient and PTRS were used as they were for trastuzumab.

Figure S5| Panitumumab clinical development event tree and simulation results

Panitumumab market data and assumptions. The panitumumab case study considered the U.S. colorectal cancer market with an annual incidence of ~150,000. A 2% annual growth in patients was also assumed. The adoption speed, unit sales (and so market share) and pricing of panitumumab in the all-comers setting was taken from Amgen annual reportsS26 and industry data sources such as IMS Health. Note that compared to trastuzumab, the lower therapeutic effect of panitumumab and higher underlying responders percentage results in a lower enrichment effect, limiting pricing, market share and adoption speed benefits. For the stratified scenario only a 15% price increase was assumed due to the modest efficacy with a similarly modest improvement in the time to peak sales and peak market share.

Legend 2 Black: Ph III All-comers Red: Ph II Companion Diagnostic Positive Only Approach: KRAS companion diagnostic known prior to clinical development Blue: Ph II All-comers with retrospective analysis: KRAS predictive biomarker discovery in Phase II retrospective analysis Green: Ph III All-comers with retrospective analysis: KRAS discovered during Phase III: Actual path



AD MODEL DATA AND ASSUMPTIONS Bapineuzumab clinical data and assumptions. As described in the main text and Box 1, the AD case study relies on less clinical information than the oncology cases since no effective commercial AD treatment yet exists. Bapineuzumab, however, has completed Phase II trialsS27-S31 and is currently being tested in Phase III clinical trialsS32. The AD case study is based on the assumption of successful Phase I and Phase II trials and so begins modeling with Phase 3 studies. In all AD cases, it was assumed that clinically relevant success would require observing an 18 month ADAS-cog improvement of 5.0 compared to Aricept’s 3.0 improvement in 24 weeks on the same scale S27-S32. Based on this success criterion, the minimum size of the studies required to demonstrate statistical significance (about 125 patients per arm) is much less than that will likely be needed to demonstrate adequate long-term safety. For a chronically administered therapy the team therefore assumed 1,000 patients with more than 12 months dose exposure would be necessary to establish an adequate safety database. Study sizes are similar to those actually proposed for bapineuzumab by Johnson & Johnson, Elan, and Pfizer S23. The number of patients varies from scenario to scenario while the time to primary endpoint remains consistent at 18 months. Clinical trial costs are assumed to be $50,000 per patient in an all-comers setting, $60K per patient if the predictive biomarker is used prospectively or retrospectively, and $65,000 per patient if only predictive biomarker positive patients are included to account for the additional recruitment costs based on team experience and Institute of Medicine benchmarking of complex oncology studiesS33. Additionally the time for regulatory review is set at 12 months consistent with PDUFA IV performance goals for review and action on a standard Biologic License Application (BLA)S34, and a cost of $12 million for the therapeutic review based on team member experience and consensus. Development is set to begin in 2009 with approval year based on the study design. To determine the probability of technical and regulatory success in the alternative development scenarios (see Box 2), data from completed Phase II trialsS27-32 were analyzed to determine the overall response rate as well as the target group response rate in terms of ADAS-cog change versus placebo. These response rates were then incorporated into a Monte Carlo simulation to determine an expected level of success in meeting various ADAS-cog improvement points. Figure S6 shows the decision tree and results development approach 4–the dual design with all-comers and companion diagnostic positive features–which contains separate arms for companion diagnostic positive and negative populations (see Box 2). The PTRS and clinical trial sizes were then used by the MIT model to create eNPVs.



Figure S6| Clinical development simulation results for bapineuzumab in development approach 4 (see Box 2): dual design with all-comers and companion diagnostic positive features. The decision tree illustrates the simulated results when the 18 month endpoint success criterion of an ADAS-cog 5 point improvement is used for a 35% prevalent non-carrier population. The table under the decision tree shows the calculated probabilities of success for each endpoint success criterion ranging from a 3 point to a 7 point ADAS-cog improvement. ApoE4 non-carriers are the higher responder population while the ApoE4 carriers are the low responder population. Bapinuezumab market data and assumptions. The case study is based upon a disease modifying, biologic therapeutics for mild to moderate Alzheimer’s disease. The model focuses on a United States only launch strategy and assumes an initial total potential population of 4 million individuals growing at a rate of 3% per year S35, S36. Additionally, the model assumes that 100% of individuals that would be responders based upon ApoE4 status would be considered qualified patients. Due to the variety of published estimates for ApoE4 prevalence in the AD population, the percent of responder patients was varied from 20% up to 55% to determine the sensitivity of the selected population size on overall valueS37, S38. The adoption speed and market share of the therapeutic are based upon the expected therapeutic benefit in the form of expected ADAS-cog improvement compared to placebo. As the expected ADAS-cog value increases the market share and price premium increase linearly. Market share is based upon the assumption that similar first in class, disease modifying products (injectable monoclonal antibodies) achieve a 70% share in the treatment of AD. Additional evidence for a high peak market share possibility in AD is provided by donepezil (Aricept: Eisai, Pfizer) whose market share reached similar levels and still maintains 50% market share in the face of multiple competitors and imminent patent lossS39, S40. From this the share of the therapeutic



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Documents

Box S1 | Modelling tools used · general purpose tool to optimize therapeutic clinical study designs with biomarkers. Clinical development simulation model for trastuzumab and panitumumab