Hands-On Learning
Gain practical experience through hands-on projects and real-world applications.
Evidence-Based Learning
Develop a structured understanding of biotech investment and financing through clear explanations, worked examples and practical decision-making frameworks.
Flexible Schedule
Study at your own pace with our online platform, allowing you to balance work and learning effectively.
About the Course
Biotechnology companies face a distinctive financing challenge: substantial capital may be required long before scientific, clinical or commercial success is known. This course provides a structured introduction to how scientific evidence, uncertainty and development milestones connect to investment and financing decisions. You will explore how biotech companies determine capital requirements, assess valuation and dilution, identify appropriate investors, evaluate financing terms, consider partnerships and understand potential exit pathways. Through worked examples, case-based activities and an integrated decision-making framework, you will learn to distinguish between company value, financing, ownership and shareholder outcomes while considering the risks and uncertainties that shape biotechnology development. The course also explores how AI can support biotech financing analysis responsibly, including evidence gathering, structured research and analytical workflows, while maintaining appropriate human judgement at consequential decision points. By the end of the course, you will have a practical framework for thinking more systematically about the relationship between evidence, uncertainty, capital and strategic decision-making in biotechnology.
Meet Your Instructor
Jonathan Wong is the founder of CloudPedagogy, where he develops practical, responsible and future-focused approaches to digital learning and emerging technology. Drawing on over 20 years of experience in higher education, research and digital innovation, his work explores how technology can support learning, analysis and knowledge creation. His work spans generative and agentic AI, digital learning design, interactive learning systems, educational technology and open-source development. A particular focus is translating emerging technologies into practical tools, workflows and learning experiences while maintaining transparency, critical judgement and human oversight. Through CloudPedagogy, Jonathan develops courses, frameworks, demonstrations and open-source projects designed to help learners and professionals understand complex ideas, experiment with new technologies and apply them in meaningful real-world contexts.
Leo Wong — Course Contributor
Leo Wong is a First-Class Biology graduate from Royal Holloway, University of London and recipient of the Joyce Pentelow Prize for academic achievement. His academic background spans molecular biology, genetics, quantitative biology, bioinformatics and environmental science. His undergraduate research included an independent project investigating recombinant expression of an ADAM-10 protein fragment, using molecular biology and bioinformatics techniques, and the development of a climate-change research proposal exploring CRISPR-edited soybean variants for climate-resilient cultivation. He also has experience using R/RStudio for biological data analysis and statistical interpretation. Leo contributes to the ongoing review and development of this course, bringing a recent bioscience graduate perspective to its scientific content, applied examples and learning activities. His involvement includes evaluating the course from a learner perspective and contributing to the development of practical and biologically relevant material. His interests include biological research, genomics, biotechnology and healthcare.
Curriculum
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Welcome to the Course Free preview
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How This Course Works Free preview
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Legal Disclaimer & Terms of Use Free preview
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About This Course & Learning Outcomes
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1.1 — From Scientific Discovery to Investable Opportunity Free preview
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1.2 — The Evidence–Uncertainty–Capital Cycle Free preview
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1.3 — Value-Inflection Points Free preview
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1.4 — The Value of Information
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1.5 — Financing a Company That May Have No Revenue
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1.6 — Development Cost Is Not the Same as Financing Need
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1.7 — Financing as a Matching Problem
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1.8 — Running Case: Vareqion Biosciences
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1.9 — Learning Check
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1.10 — Summary
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2.1 — There Is No Single Biotech Funding Path
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2.2 — A Framework for Comparing Sources of Capital
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2.3 — Founder and Internal Capital
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2.4 — Grants and Other Non-Dilutive Funding
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2.5 — Angel Investment
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2.6 — Venture Capital
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2.7 — Corporate and Strategic Capital
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2.8 — Mission-Driven Capital
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2.9 — Debt Financing
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2.10 — Public Markets
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2.11 — Combining Sources of Capital
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2.12 — Ownership, Dilution and the Cap Table
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2.13 — Building a Financing Strategy Around the Next Milestone
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2.14 — Application Connection: Capital and Runway
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2.15 — Learning Activity: Financing Vareqion
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2.16 — Learning Check
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2.17 — Summary
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3.1 — How Venture Capital Works
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3.2 — The Economics of a Venture Fund
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3.3 — Why Fund Size Matters
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3.4 — Portfolio Construction and Return Requirements
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3.5 — Investment Mandates and Investor Fit
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3.6 — Investment Stage and Ticket Size
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3.7 — Follow-On Capacity
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3.8 — Fund Lifecycle and Investment Horizon
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3.9 — How Investors Evaluate Biotech Opportunities
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3.10 — The Investment Case
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3.11 — Pitching to Investors
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3.12 — Investor Due Diligence
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3.13 — Due-Diligence Readiness
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3.14 — Founder Due Diligence on Investors
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3.15 — Evaluating Investor Behaviour and Fit
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3.16 — Application Connection: Investor Research
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3.17 — Learning Activity: Research an Investor
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3.18 — Learning Check
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3.19 — Summary
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4.1 — Start with What the Capital Must Accomplish
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4.2 — How Much Should the Company Raise?
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4.3 — Financing Resilience
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4.4 — When to Start Fundraising
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4.5 — Building the Investor Map
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4.6 — Sequencing Investor Approaches
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4.7 — The Initial Investor Pitch
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4.8 — From Investor Interest to Financing
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4.9 — Due Diligence and the Financing Process
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4.10 — Understanding the Term Sheet
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4.11 — Pre-Money and Post-Money Valuation
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4.12 — Ownership and Dilution
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4.13 — Share Classes and Investor Rights
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4.14 — Liquidation Preferences
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4.15 — Anti-Dilution Protection
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4.16 — Employee Option Pools
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4.17 — Pro-Rata and Future Financing Rights
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4.18 — Governance, Board Seats and Consent Rights
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4.19 — Exclusivity and Strategic Restrictions
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4.20 — Convertible Financing
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4.21 — Model the Whole Offer
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4.22 — Comparing Two Offers
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4.23 — Legal Advice Is Essential
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4.24 — Application Connection: Why the Workflow Does Not Negotiate Term Sheets
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4.25 — Learning Activity: Compare Two Term Sheets
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4.26 — Learning Check
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4.27 — Summary
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5.1 — Value, Valuation and Price
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5.2 — Why Early Biotech Is Difficult to Value
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5.3 — Evidence, Uncertainty and Value
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5.4 — Asset-Based Valuation
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5.5 — Discounted Cash Flow
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5.6 — Discount Rates and the Cost of Capital
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5.7 — Enterprise Value and Equity Value
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5.8 — Terminal Value
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5.9 — Why Ordinary DCF Can Be Problematic in Biotech
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5.10 — Risk-Adjusted Net Present Value
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5.11 — Development Probabilities
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5.12 — Probability-Adjusted Development Costs
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5.13 — Comparable Companies
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5.14 — Comparable Transactions
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5.15 — Selecting Meaningful Comparables
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5.16 — The Venture Capital Method
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5.17 — Investor Return Requirements
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5.18 — Future Financing and Dilution
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5.19 — Why a Higher Valuation Is Not Always Better
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5.20 — Triangulating Valuation Methods
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5.21 — Sensitivity and Scenario Analysis
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5.22 — Making Valuation Assumptions Explicit
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5.23 — Application Connection: Valuation and the Workflow
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5.24 — Learning Activity: Compare Valuation Methods
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5.25 — Learning Check
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5.26 — Summary
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6.1 — Cash Is a Development Resource
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6.2 — Burn Rate
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6.3 — Gross Burn and Net Burn
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6.4 — Revenue Does Not Automatically Extend Runway by Its Full Amount
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6.5 — Cash Runway
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6.6 — Why Average Burn Can Mislead
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6.7 — Month-by-Month Cash Forecasting
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6.8 — Programme Cost Versus Financing Requirement
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6.9 — Post-Milestone Runway
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6.10 — Absolute Runway and Fundraising Runway
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6.11 — Fundraising Timing
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6.12 — Waiting for Evidence: The Financing Trade-Off
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6.13 — Contingency
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6.14 — Contingency Is Not Permission to Continue After Failure
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6.15 — The Economic Value of Failing Early
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6.16 — Capital Efficiency
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6.17 — Revenue-Generating Side Activities and Opportunity Cost
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6.18 — Lumpy Expenditure
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6.19 — Financing Rounds and Financing Tranches
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6.20 — Scenario Analysis
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6.21 — Financing Resilience
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6.22 — Building the Integrated Capital Requirement
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6.23 — Application Connection: Deterministic Runway and Capital Calculations
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6.24 — Learning Activity: Stress-Test Vareqion's Runway
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6.25 — Learning Check
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6.26 — Summary
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7.1 — A Company Does Not Need to Own Every Capability
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7.2 — Why Partner?
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7.3 — Complementarity
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7.4 — The Partnership-Fit Framework
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7.5 — Licensing
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7.6 — Exclusive and Non-Exclusive Licensing
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7.7 — Option-to-License Structures
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7.8 — Co-Development
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7.9 — Research Collaborations
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7.10 — Transaction Economics
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7.11 — Upfront Payments
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7.12 — Milestone Payments
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7.13 — Royalties
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7.14 — Corporate Venture Capital
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7.15 — Why Corporations Invest
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7.16 — Strategic Interest Can Change
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7.17 — Promised Resources Versus Accessible Resources
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7.18 — Information Exposure
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7.19 — Strategic Optionality
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7.20 — Mission-Driven and Catalytic Capital
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7.21 — Family Offices and Patient Capital
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7.22 — Private Equity and Why Classic LBOs Rarely Fit Early Biotech
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7.23 — Primary and Secondary Transactions
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7.24 — Exit and Liquidity
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7.25 — Acquisition
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7.26 — Initial Public Offering
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7.27 — Exit Optionality
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7.28 — Headline Exit Value Versus Shareholder Proceeds
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7.29 — Investor Exit and Founder Outcome Are Different
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7.30 — Build, Partner or Exit?
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7.31 — The Partnership–Financing–Exit Continuum
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7.32 — The Capital-Provider Fit Framework
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7.33 — Application Connection: Capital Matching and Investor Research
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7.34 — Learning Activity: Choose a Strategic Path
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7.35 — Learning Check
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7.36 — Summary
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8.1 — The Complete Evidence–Uncertainty–Capital Cycle
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8.2 — Start with the Evidence
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8.3 — Identify the Critical Uncertainty
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8.4 — Define the Evidence Required
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8.5 — Select the Value-Inflection Point
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8.6 — Translate the Milestone into a Development Programme
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8.7 — Determine Time and Capital Requirements
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8.8 — Stress-Test the Financing Requirement
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8.9 — Match the Company to Appropriate Sources of Capital
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8.10 — Research and Evaluate Specific Investors
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8.11 — Evaluate Valuation, Ownership and Transaction Terms
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8.12 — Consider Strategic Alternatives
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8.13 — Preserve Future Optionality
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8.14 — Reassess When New Evidence Arrives
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8.15 — Building the Integrated Financing Decision
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8.16 — Responsible Use of AI in Biotech Financing
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8.17 — Evidence Before Inference
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8.18 — Deterministic Where Possible
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8.19 — Human Review at Consequential Judgement Points
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8.20 — Agentic AI Where External Investigation Is Required
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8.21 — The Practical Workflow Architecture
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8.22 — The Role of the Practical n8n Project
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8.23 — Final Integrated Case Exercise
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8.24 — Final Learning Check
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8.25 — Course Summary
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Course Complete: Share Your Feedback
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End of Course Survey
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