Wise Investment? Modeling Industry Profitability and Risk of Targeted Chemotherapy for Incurable Solid Cancers
Bibliographic record
Abstract
PURPOSE: Pharmaceutical development involves substantial financial risk. This risk, rising development costs, and the promotion of continued research and development have been cited as major drivers in the progressive increase in drug prices. Currently, cost-effective analyses are being used to determine the value of treatment. However, cost-effective analyses practically function as a threshold for value and do not directly address the rationale for drug prices. We set out to create a functional model for industry price decisions and clarify the minimum acceptable profitability of new drugs. METHODS: Assuming that industry should only invest in profitable ventures, we employed a linear cost-volume-profit breakeven analysis to equate initial capital investment and risk and post-drug-approval profits, where drug development represents the bulk of investment. A Markov decision analysis model was also used to define the relationships between investment events risk. A systematic literature search was performed to determine event probabilities, clinical trial costs, and total expenses as inputs into the model. Disease-specific inputs, current market size across regions, and lengths of treatment for cancer types were also included. RESULTS: With development of single novel chemotherapies costing from $802 to $1,042 million (2002 US dollars), pharmaceutical profits should range from $4.3 to $5.2 billion, with an expected rate of return on investment of 11% annually. However, diversification across cancer types for chemotherapy can reduce the minimum required profit to less than $3 billion. For optimal diversification, industry should study four tumor types per drug; however, nonprofit organizations could tolerate eight parallel development tracks to minimize the risk of development failure. Assuming that pharmaceutical companies hold exclusive rights for drug sales for only 5 years after market approval, the minimum required profit per drug per month per patient ranges from $294 for end-stage lung cancer to $3,231 for end-stage renal cell carcinoma. CONCLUSION: Pharmaceutical development in oncology is costly, with substantial risk, but is also highly profitable. Minimum acceptable profits per drug per month of treatment per patient vary with prevalence of disease, but they should be less than $5,000 per month of treatment in the developed world. Minimum acceptable profits may be lower for treatments with additional efficacy in the earlier stages of a tumor type. However, this type of event could not be statistically modeled.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".