This project is an interactive decision-support simulator designed to explore strategic trade-offs in pharmaceutical R&D portfolio allocation.
Rather than optimizing a single metric or predicting outcomes, the simulator evaluates how different portfolio configurations perform across competing strategic objectives, including speed to market, capital efficiency, learning optionality, and expected approvals.
The goal is structural insight, not prescription.
🔗 Interactive simulator (ShinyApps.io):
Pharma R&D Simulator
Selected views illustrating the strategic framework and decision-support structure:
Strategic priority selection with hypothesis-driven recommendations
Interactive resource allocation with real-time trade-off feedback
Multi-scenario comparison using normalized radar charts
The simulator helps answer questions such as:
- When do strategic objectives align versus conflict?
- Under what conditions does specialization outperform diversification?
- How do therapeutic area risk profiles alter portfolio outcomes?
- What trade-offs emerge when prioritizing learning versus near-term output?
This tool is intended for exploratory analysis and strategic discussion, not investment decision-making.
To avoid overreach, the simulator explicitly does not:
- Predict individual asset success or failure
- Recommend investment levels or portfolio sizes
- Model company-specific pipelines
- Estimate financial return, valuation, or NPV
- Optimize portfolios against a single objective
All results are conditional on assumptions and support structured thinking, not conclusions.
Portfolios are evaluated across five strategic objectives, each representing a testable hypothesis about organizational priorities:
- Maximize Expected Approvals
- Minimize Risk (Predictability)
- Maximize Learning Optionality (early-stage exposure)
- Speed to Market
- Capital Efficiency (approvals per $100M)
There is no single “correct” objective — trade-offs are expected and intentional.
The simulator evaluates multiple portfolio configurations that vary by:
- Stage allocation (early vs late)
- Therapeutic focus
- Development timelines
- Cost structures
These scenarios use published transition probabilities and timelines:
- Overall (industry average)
- Oncology
- Vaccines
- Anti-Infectives
Additional scenarios (e.g., Fast-Track CNS, Rare Disease Focus, Biologics Heavy) apply transparent modeling assumptions on top of empirical baselines to explore “what-if” strategic postures.
Baseline risk and attrition dynamics are drawn from peer-reviewed and industry sources:
- Wong et al. (2019)
- Norstella (2024)
- ACSH (2020)
Where published data does not exist, assumptions are applied via explicit multipliers (e.g., faster timelines, higher success rates). These are:
- Documented
- Adjustable
- Intended for sensitivity testing, not realism claims
To enable comparison across objectives with different units:
- Performance metrics are min–max normalized across selected scenarios
- Radar charts visualize relative performance, not absolute magnitude
- Larger area indicates stronger performance within the comparison set
Normalization does not imply equal importance across objectives.
- Multi-objective, not single-metric optimization
- Transparency over complexity
- Explainability over black-box scoring
- Decision support, not automation
- Assumption-aware results
These principles mirror real-world portfolio discussions.
- Results are expectation-based, not probabilistic forecasts
- Learning is proxied by early-stage exposure, not scientific output
- Strategic scenarios rely on simplified assumptions
- No competitive or market dynamics are modeled
These limitations are intentional to preserve interpretability.
Designed for:
- R&D strategy and portfolio management
- Trial analytics and insights teams
- Decision-support and simulation practitioners
- Analysts interested in multi-objective trade-off frameworks
This simulator demonstrates how portfolio outcomes depend less on optimization and more on explicitly stated priorities.
By making trade-offs visible, it supports clearer, more disciplined strategic conversations.
Pharma_R&D/
├── app.R
├── global.R
├── server.R
├── ui.R
│
├── modules/
│ ├── mod_executive_brief.R
│ ├── mod_portfolio_builder.R
│ ├── mod_tradeoff_explorer.R
│ └── mod_methods.R
│
├── R/
│ ├── simulation_functions.R
│ ├── scenario_builder.R
│ └── setup_runtime.R
│
├── data/
│ ├── processed/
│ │ ├── phase_transitions.rds
│ │ ├── trial_durations.rds
│ │ ├── cost_estimates.rds
│ │ └── therapeutic_areas.rds
│ └── scenarios/
│ └── preset_scenarios.rds
│
├── screenshots/
└── README.md
This project is released under the MIT License.
This project is for educational and exploratory purposes only.
It does not constitute financial, investment, or clinical advice.
This simulator is not affiliated with, endorsed by, or produced by any pharmaceutical company.
All parameters are derived from publicly available peer-reviewed literature. No representation is made regarding applicability to specific company contexts.
Steven Ponce
Data Analyst | R Shiny Developer | Business Intelligence Specialist
🔗 Portfolio Website: stevenponce.netlify.app
🐙 GitHub: @poncest
💼 LinkedIn: stevenponce
🐦 X (Twitter): @sponce1
Prepared by Steven Ponce as part of a professional analytics portfolio.
Demonstrating strategic thinking, simulation methodology, and executive decision-support design.