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1 Box S2 Investment model A seminal 1997 study by Meyers and Howe suggested that drug R&D represented a marginal investment when analysed using a risk-adjusted, fully discounted model 1. A more recent study 2 by authors at McKinsey & Company suggested that the internal rate of return (IRR) for small-molecule R&D has declined further, falling from ~12% in to ~7.5% in Given their estimates for the cost of capital, the authors asserted that small-molecule R&D investment carried a negative net present value (NPV; in this case, NPV = $65 million). Notably, the same study suggested that investment in biologics drug discovery provided a markedly superior return (IRR ~13%) 2. As described in detail in Box S1, the IPO market for companies was robust in the timeframe and investors were willing to put capital at risk to fund projects based on both small molecules and biologics. Given that this behaviour did not match hypotheses generated by previously published investment models, we constructed our own model to understand in a quantitative way what assumptions could meaningfully change the predicted financial outcome. We needed a model that was granular enough to evaluate significant differences between small molecules and biologics, and therefore chose a single molecule framework, beginning at preclinical development (i.e. candidate identification to first-in-human dosing) and extending through the several years of sales following loss of exclusivity. Nine stages were modelled in all (see Table S4 below and the Excel spreadsheet for the model). Although different methods can be employed for estimation of the project s net present value (NPV), we used a standard decision tree approach 3. This method assumes that the project advances to the next stage (and incurs the associated costs) only in the event of success in the current stage, thereby providing the analysis with an option-like character. The backbone of the model is the month-by-month estimate of pre-tax net revenue in real 2008 dollars. This cash flow is discounted back to the start of each stage on a monthly basis, and then subsequently back to the start of the model. The compartmentalization of the stages enables flexibility in regards to both variable discount rate structures and the period studied for the NPV estimation, even though our baseline variable set employs a single discount rate for the entire model. Finally, as described above, this discounted cash flow is adjusted for probability of phase transition. Summation of the probability-adjusted discounted cash flow yields the NPV estimation. The same model structure can be used to calculate internal rate of return (IRR) by determining the single discount rate required to achieve an NPV of zero. We constructed the model as an analytical tool, and therefore associated most assumptions with input variables, such that the effect of altered assumptions could be assessed. As shown in Table S4, the input variables for the five R&D stages were cycle time, cost, and probability of transition to the next stage; and we capitalized on the work of DiMasi et al. 4, Paul et al. 5, Hay et al. 6 and Horvath 7 in order to establish a baseline variable set for small molecules and biologics. We assumed 0% real growth in costs and a 9.5% annual real discount rate for the baseline, and examined the impact of each in separate sensitivity analyses. More inputs were needed to construct the monthly revenue model for the commercial stages (Table S4). All four phases depended in some way on the estimate of US peak sales, a global sales multiplier 8 and pre-tax profit margin #. Launch costs were estimated as a fraction of peak sales. The shape of the ramp from launch to peak 9 was modelled from historical data 10 in three linear segments, which enabled sufficient differentiation between the small molecule and biologic. A global launch delay was enabled as a variable 11, as was the possibility of failure to enter the peak sales period due to product withdrawal 12. The length of the plateau at peak was determined by the exclusivity period 13,14, and real growth in peak sales was set to 0% in the baseline set. The loss of exclusivity (LOE) for small molecules was patterned after historical data, and a post-loe period of tail sales was built into the model. The biologic pattern for LOE was patterned after 2000-era small-molecule data, with terminal sales of 33%, assuming modest biosimilar competition. 1

2 Table S4 Baseline assumptions for input variables in the investment model a Stage Variable Small molecule Biologic References Preclinical b Time 12 months 18 months Paul, Horvath Cost c $5m $6.5m Paul, Horvath pts Paul, Baldrick Phase 1 Time 20 months 20 months DiMasi Cost c $15m $17.7m Paul, Horvath pts Hay Phase 2 Time 30 months 30 months DiMasi Cost c $40m $42.7m Paul, Horvath pts Hay Phase 3 Time 30 months 30 months DiMasi Cost c $150m $147m Paul, Horvath pts Hay Filing Time 16 months 16 months DiMasi Cost c $40m $40m Paul pts Hay Launch US Peak Sales c $388m $415m Berndt; Evaluate Global multiplier IMS Pre-tax profit margin 50% 50% 10K calculation Launch cost 25% peak 25% peak Inferred Global launch delay 6 months 6 months One reference Time to 30% peak 9 months 12 months Berndt/Trusheim Time from 30 60% peak 15 months 24 months Berndt/Trusheim Time from % 36 months 30 months Berndt/Trusheim peak pts Several refs Peak Exclusivity period 150 months post approval 144 months post approval Grabowski, Osborne LOE %Peak at 1 mo LOE 37% 65% Grabowski + inferred %Peak at 6 mo LOE 19% 50% As above Global LOE delay 6 months 6 months Parallels launch delay Stage length 18 mo+global 18 mo+global Grabowski+inferred Delay Delay %Peak at stage end 11% 33% Grabowski+inferred Tail Stage length 18 mo, less 36 mo, less Berndt Global delay Global delay All R&D Discount rate 9.5% 9.5% Several All companies Real rate of cost growth 0% 0% Starting point Discount rate 9.5% 9.5% Several Real rate of peak growth 0% 0% Starting point a The table lists the input variables utilized in the investment model, along with the baseline assumptions for both small molecules and biologics. References for each assumption are provided. b Preclinical development is defined as the stage from candidate identification to first-in-human dosing. Preclinical discovery research is not included. c The analysis was conducted in real 2008 dollars. The key outputs from the model are summarized in Table S5 (baseline inputs utilized). As shown, the total real cash flow for a successful drug project is ~10% higher for a biologic than a small molecule, driven primarily by sales revenue differences. Since most of these differences occur much later in the lifetime of the compound, discounting the cash flow back to the preclinical stage provides relatively similar present values. However, when one accounts for the published average success rates for small molecules and biologics, the probability-adjusted, fully discounted NPV proves to be substantially 2

3 higher for the latter, with $37 million and $104 million estimated as the NPV for single asset smallmolecule and biologic projects, respectively. These correspond to internal rates of returns of 15.7% and 18.9%. Table S5 Summary model output using baseline assumptions a Small Molecules Biologics End, mo Cum P en Net, $m PV i, $m P- PV i, $m End, mo Cum P en Net, $m PV i, $m P- PV i, $m Pre- clin (5) (5) (5) (7) (6) (6) Ph (15) (13) (9) (18) (14) (12) Ph (40) (28) (13) (43) (29) (18) Ph (150) (84) (14) (147) (78) (22) Filing (40) (19) (2) (40) (18) (4) Launch & Ramp , , Peak Plateau , , LOE Tail TOTAL, $m 4, , a The table shows the summary output for the investment model, segregated by modality. The baseline assumptions specified in Table S4 were utilized as inputs. End = cumulative time at end of phase. Cum Pen = cumulative percentage chance of entering phase. Net = net revenues (cost) in phase, in millions of 2008 dollars; this column sums to the undiscounted cash flow for a successful project. PVi = net revenues (cost) in phase, discounted monthly to the start of preclinical at an annual rate of 9.5%, in millions of 2008 dollars; this column sums to the discounted cash flow for a successful project. P-PVi = probability-adjusted discounted net revenue; this column sums to Net Present Value at the start of the project. We used sensitivity analysis in order to probe the importance of each non-rate variable to the estimate of economic value, and selected the small-molecule model as a test case. An improvement of 15% (e.g. increase in the probability of technical success (pts)** or decrease in time or cost) had a markedly different effect on NPV depending on the individual variable altered, and only 15 of the 31 variables yielded an improvement of 5% or more when changed (Figure S2). The most sensitive variables were in the commercial and late clinical phases. Within the R&D phases, alteration of probability of success had a larger impact than changes in either time or cost, and indeed the Phase 3 and Phase 2 probability of phase transition were the fourth and sixth most impactful variables in the model (Figure S2). Three of the top five most sensitive variables related to the revenues from peak sales: profit margin, US peak revenue, and global multiplier. Although the use of average revenue clearly limits the utility of this model for valuing any individual project, it is of concern that the ability to accurately forecast returns for any given drug is quite low, even at times close to launch 15. Given that both peak sales and profit fraction are variables with high sensitivity in the model, it is also of concern that a recent study has shown that peak sales are declining but cost of goods are not 8. Filing- Time Ph2- Cost Ph3- Cost Ph1- Time Ph2- Time Ph3- Time MEP Pre- Clin- pts Ph1- pts Ph2- pts Global Mult Ph3- pts Peak Sales Filing- pts Profit Margin Increase in NPV ($m) Figure S2 Small-molecule NPV sensitivity analysis. Thirty-one baseline input variables for the Small Molecule NPV estimation (from the pre-clinical stage) were individually changed by 15% in a way that would improve the NPV. The top fifteen results, each of which provided an improvement of more than 5% in the baseline value of $37 million, are shown here. The three rate variables (discount rate, cost growth, peak growth) were not included in this analysis. 3

4 We also probed the sensitivity of the model to the three rate variables: discount rate, rate of R&D cost increase, and rate of peak sales increase, all of which were expressed in real terms. Not surprisingly, the model was quite sensitive to the choice of discount rate (Figure S3A). Relative to the variables shown in Figure S2, a 15% decrease in the real discount rate (to ~8.1%) increased the estimate of small molecule NPV by +$15.9 million, which would therefore rank it as the second most sensitive variable. Also not surprising was the sensitivity of the model to the real rate of increase in peak sales (Figure S3B), given both the importance of peak sales and the compounding of the rate s effect over the course of the R&D phases. Although we assumed 0% as the baseline assumption for the real rates of increase in R&D cost and peak sales, it should be noted that the trends over the past several years suggest those assumptions are likely accurate only for peak sales (which have been flat or declining on a real basis) and possibly a significant underestimate for R&D costs. Although the financial model presented here is not exceptionally sensitive to R&D cost, changes of the magnitude observed historically (~ 6 8%) 4 would be large enough to generate a very meaningful reduction in NPV (Figure S3B). A 120 B NPV, $m NPV, $m R&D Cost Peak Sales Discount Rate Real Growth Rate Figure S3 Sensitivity of NPV to rate variables. Shown are the effects of changes in rate variables on small-molecule NPV, as estimated at the start of the preclinical stage. A The discount rate was varied in 1.0% increments from 4.5 to 14.5%. B The real growth rates for R&D cost (blue) and peak sales (red) were varied in 1.0% increments from -3 to +6%. Since assumptions about the discount rate, time and pts have a substantial impact on the financial model, we calculated the NPV from different starting points. As expected, the NPV increased sharply for both small molecules and biologics when starting later in the process (Table S6), since the calculations assumed that previous costs were sunk. Given that the difference in NPV between biologics and small molecules at the start of preclinical development was driven in large part by differences in success rates, it is not surprising that the percentage difference between these two modalities shrinks dramatically when one starts the analysis from a later stage (Table S6), at which time more risk has been discharged. This is true even in the face of a longer period of sales for the biologic. Table S6 Effect of starting phase on NPV estimation for the project a Model Start Small Molecule Biologic %Change Cum T, mo Cum P(TS) NPV, $m Cum T, mo Cum P(TS) NPV, $m Biol- SM Pre- clin % Ph % Ph % Ph % Filing , ,687 10% Launch , ,212 2% a The table shows the summary output for the single compound investment model when starting from different points in the R&D process. The output is segregated by modality. The baseline assumptions specified in Table S4 were utilized as inputs. Start = starting point for valuation. Cum T = cumulative time for the project from the starting point to the end of the model. Cum P(TS) = cumulative probability of technical success. NPV = probability-adjusted discounted net present value for the project, as estimated at the indicated starting point. %Change = increase in NPV of biologic relative to small molecule, expressed as a percentage of the small molecule NPV. 4

5 Finally, in light of the large impact of the discount rate on the model, we examined the role of differential discounting. Myers and Howe previously argued that pharmaceutical revenues should be discounted back to model inception (i.e. not just within the commercial phase) at a higher rate (9%) than the rate used to discount R&D costs (6%), on the basis that the latter were more certain and represented a mortgage-like obligation 1. Using the values assumed by Myers and Howe yielded a similar NPV to the single-discount method ($36.8 million versus $37.0 million); a small-scale sensitivity analysis showed that this two-tiered discounting method was very sensitive to the choice of the rate of revenue discounting and relatively insensitive to the rate of cost discounting (Table S7). Table S7 Effect of differential discounting of costs and revenues on NPV estimation a R(revenues) 8% 9% 10% 4% R(costs) 5% % a The table shows the summary output for the single compound investment model when using a different rate to discount the revenues back to model start than was used to discount R&D costs back to model start. An alternative approach to differential discounting is to apply different discount rates to different research phases, in line with the observable changes in cost of capital for companies engaged in different phases of pharmaceutical research 16. We converted the nominal cost of capital derived by Baras et al. 16 to real rates for preclinical (15.7%), clinical (11.5%), and commercial (6.7%) phases, and applied them such that revenues from commercial phases were subject to three different discount rates when the model was started at the pre-clinical stage. This approach yielded a small-molecule NPV that was ~8% higher than the single-rate method ($39.8 million versus $37.0 million). This method was particularly sensitive to the time of start of the model, and so we compared this method directly to the single rate method when examined from different model start points (Table S8). Table S8 Comparison of three-tiered discounting versus single rate discounting for different model starting points a Model Start Small Molecule NPV, $m Single Multiple Fold Pre- clin Ph Ph Ph Filing 1,533 1, Launch 2,161 2, a The table shows the summary output for the single compound investment model when using a three-tiered rate structure relative to the previously shown single-rate discounting (9.5%). In the three-tiered model, monthly cash flows were discounted back to the phase start for the rate in period and back to the model start using different rates for each time period. Real rates used for each phase were 15.7% for preclinical, 11.5% for all clinical phases, and 6.7% for all commercial phases. 5

6 References 1. Myers SC, Howe CD. A life-cycle financial model of pharmaceutical R&D. MIT Program on the Pharmaceutical Industry (MIT Sloan School of Management, 1997). 2. David E, Tramontin T, Zemmel R. Pharmaceutical R&D: the road to positive returns. Nat Rev Drug Discov. 2009; 8: Hartmann, M., Hassan, A. Application of real option analysis for pharmaceutical R&D project evaluation empirical results from a survey. Research Policy 2006; 35: DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 2016; 47: Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL. How to improve R&D productivity: the pharmaceutical industry s grand challenge. Nat Rev Drug Discov. 2010; 9: Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014; 32: Horvath C. Comparison of preclinical development programs for small molecules (drugs/pharmaceuticals) and large molecules (biologics/biopharmaceuticals): studies, timing, materials, and costs. Pharmaceutical Sciences Encyclopedia Berndt ER, Nass D, Kleinrock M, Aitken M. Decline in Economic Returns from New Drugs Raises Questions About Sustaining Innovations. Health Affairs 2015; 34: Fischer M, Leeflang PSH, Verhoef PC. Drivers of peak sales for pharmaceutical brands. Quant Mark Econ 2010; 8: Trusheim M, Aitken ML, Berndt ER. Characterizing Markets for Biopharmaceutical Innovations: Do Biologics Differ from Small Molecules? NBER Working Paper No ; May Heuer A, Mejer M, Neuhaus J. The national regulation of pharmaceutical markets and the timing of new drug launches in Europe, Kiel advanced studies working papers, No. 437; Frank C, Himmelstein DU, Woolhandler S, Bor DH, Wolfe SM, Heymann O, Zallman L, Lasser KE. Era of faster FDA drug approval has also seen increased black-box warnings and market withdrawals. Health Aff (Millwood). 2014; 33: Grabowski H, Long G, Mortimer R. Recent trends in brand-name and generic drug competition. J Med Econ. 2014; 17: Grabowski H, Long G, Mortimer R. Data exclusivity for biologics. Nat. Rev. Drug Disc. 2011; 10: Cha M, Rifai B, Sarraf P. Pharmaceutical forecasting: throwing darts? Nat. Rev. Drug Disc. 2013; 12: Baras AI, Baras AS, Schulman KA. Drug development risk and the cost of capital. Nat Rev Drug Discov. 2012; 11: Footnotes Peak US sales estimates for small molecules and biologics were derived from a detailed white paper on the subject 17. These estimates were remarkably consistent with the most recent estimates for 5-year US sales from Evaluate Pharma (see Further information). A five year average of the Evaluate Pharma data was $399 million, right in between the average values used for small molecules and biologics (Table S4) based on the earlier study. This suggests that the real rate of growth of US peak sales is close to zero. 6

7 # We tabulated data from the K filings from large capitalization pharmaceutical and biotechnology companies and determined that COGS and SG&A represent about 50% of the revenues derived from pharmaceutical sales, on average. We applied the remaining margin (50%) for both biologics and small molecules. ** This variable is particularly sensitive to the calculation methodology. Here we utilize a straight increase in probability of technical success (pts) for a given phase, which translates to a corresponding 15% increase in overall success rate. However, methods that reduce failure rates yield results that change the order of sensitivity of the different clinical phases, with Phase 2 appearing to be the most sensitive. Since the model assumes patent filing prior to start of pre-clinical, it should be noted that a change in time also results in an effective increase in the sales exclusivity period, which itself was the sixth most sensitive variable. Further information World Preview 2014, Outlook to Evaluate Pharma; June