Protein Chemistry

day 8 part 1

🧠 Big Picture: What This Lecture Is About

This lecture explains how modern drug discovery and development works, especially in a large pharmaceutical company context. The key themes are:

Why certain diseases are targeted
How drugs are developed from idea → market
Why it is extremely expensive and risky
How science, business, and logistics are tightly connected

🧬 1. Disease Focus & Why It Matters

Core idea:

Drug development is not just scientific, it is driven by:

Disease prevalence
Societal cost
Market potential
Biological feasibility

Major disease areas:

Diabetes
Obesity
Rare diseases

Key insight:

These are interconnected diseases (comorbidities):

Obesity → diabetes, cardiovascular disease, liver disease
Treating one often affects others

👉 So modern drug design aims to:

Not just treat a disease, but reduce overall health burden

⚠️ 2. The Challenge of Rare Diseases

Important concept:

~7000 rare diseases exist
Affect ~350 million people globally
BUT: very few patients per disease

Problem:

Drug cost ≈ same as common diseases (~€2B)
Few patients → very expensive treatment per person

Example:

Hemophilia treatment: €1.5–3 million/year
Obesity treatment: ~€2000/year

👉 This creates a paradox:

Scientifically possible
Economically difficult

🧪 3. Drug Modalities (Technologies Used)

Different types of drugs:

🧬 Biological

Proteins
Peptides
Antibodies

🧫 Genetic / molecular

RNA interference (RNAi)
Gene therapy

⚗️ Chemical

Small molecules (traditional drugs)

Key concept: Scalability

You must design drugs that can be produced at massive scale
Especially for diseases like obesity (millions of patients)

👉 This affects decisions VERY early, even before the drug exists.

💊 4. Route of Administration (Why it Matters)

Options:

IV (intravenous)
Subcutaneous injection
Oral (pill)

Important trade-off:

Oral drugs = easier for patients
BUT:
- Lower bioavailability
- Requires more drug material

👉 This directly impacts:

Manufacturing cost
Feasibility

🧠 5. Role of Data & AI

Modern drug development relies heavily on:

Clinical trial data
Real-world patient data
External datasets

Why?

To:

Identify target populations
Optimize trial design
Predict outcomes

👉 AI is used to:

Mine large datasets
Improve decision-making

🧪 6. Clinical Trials Scale

Example numbers:

200+ trials
~40,000 patients
~14,000 sites

👉 Key insight:

Running trials is a massive logistical operation, not just science.

🔄 7. Drug Development Pipeline (Core Concept)

This is the most important section.

Stages:

Idea / Pre-project
Research → candidate selection
Preclinical (animals)
Clinical trials
- Phase 1: safety
- Phase 2: efficacy (does it work?)
- Phase 3: large-scale validation
Submission & approval
Market launch

🧠 Key concept: “Working backwards”

You define:

What the final drug should do (label claims)

Then plan backwards:

What experiments prove that?
What data is needed?

👉 This is goal-driven science, not exploratory.

🚪 8. “Gates” (Decision Points)

Each stage has a go/no-go decision:

Gate 0 → idea validated
Gate 1 → candidate selected
Gate 2 → efficacy proven
Gate 3 → large trials
Gate 4 → submission

👉 At each gate:

Risk is evaluated
Investment increases

💸 9. Cost & Time

Key numbers:

~$2.6 billion per drug
~15 years (historically)
Goal: reduce to ~5–6 years

Why so expensive?

Because of failure rate:

Only ~2% of ideas reach market
Only ~5% survive after first human trials

👉 You are paying for:

All failed drugs
Not just the successful one

📉 10. Probability of Success (Critical Insight)

Typical success rates:

Stage	Success
Pre-project → candidate	~70%
Candidate → human	~70%
Phase 1 → 2	~40%
Phase 2 → 3	~30%
Phase 3 → submission	~60%
Submission → approval	~95%

👉 Overall:

Extremely low total probability (~2%)

⚠️ 11. Risk Management (“De-risking”)

A huge part of the job is:

Increasing probability of success
Reducing uncertainty early

Example:

Animal studies to predict clinical outcomes

👉 Goal:

Shift probabilities from e.g. 40% → 50%

Even small improvements = huge financial impact.

🏭 12. Manufacturing Challenge

You must decide:

👉 When to build large-scale production?

Problem:

Too early → waste billions if drug fails
Too late → delay market entry

This is a strategic gamble.

⏱️ 13. Why Development Is Slow

Not just science.

Major delays come from:

Regulatory approval
Setting up clinical trials
Patient recruitment
Data analysis

Key concept: “White space”

Time where:

No patient is receiving drug

👉 Reducing this = faster development

🧪 14. Parallel Development Strategy

To speed up:

Develop multiple candidates simultaneously
Run steps in parallel instead of sequentially

👉 Trade-off:

Faster
More expensive
Higher risk

💉 15. Devices Matter

Drug ≠ only molecule

Also includes:

Injection systems
Smart devices
Patient usability

Important:

Device approval is almost as complex as drug approval

🧠 16. Organizational Structure

Each drug is treated as:

An independent project with its own team

Includes:

Scientists
Clinicians
Manufacturing experts
Regulatory specialists
Commercial teams

📊 17. Portfolio Strategy

Companies manage:

Many projects at different stages

Goal:

Balance risk
Ensure some reach market

⚖️ 18. Internal vs External Innovation

Strategies differ:

Some companies:
- Do everything internally
Others:
- Buy startups / technologies

👉 No single “best” approach

🧠 Key Takeaways (Most Important Concepts)

1. Drug development is a risk-management problem

Not just science.

2. Failure is expected

Most drugs fail
Cost is built into system

3. Decisions are made with incomplete data

Early investment happens before certainty

4. Scalability and economics matter from the start

Not just “does it work?”
But also:
- Can we produce it?
- Can we afford it?

5. Speed vs risk trade-off

Faster = more risk
Slower = lost opportunity

6. Modern drug discovery is interdisciplinary

Biology
Chemistry
Data science
Logistics
Economics

Quiz

Score: 0/31 (0%)

Q0. What is the main strategic reason for focusing on obesity together with diabetes and related comorbidities?

Because obesity is only a cosmetic problem and therefore easy to treat

Because obesity often contributes to diabetes, cardiovascular disease, liver disease, kidney disease, and other complications

Because obesity drugs require no clinical trials

Because obesity treatments are only needed for adults without other diseases

Q1. Why is drug development for rare diseases economically difficult even when the biology is understood?

Rare disease drugs are always cheaper to develop than diabetes drugs

Rare diseases usually affect many millions of patients per disease

The development cost can be similar to common-disease drugs, but the number of patients is much smaller

Rare disease drugs do not require manufacturing

Q2. What does the speaker mean by 'obesity and diabetes and'?

That obesity and diabetes should be treated as completely unrelated disorders

That obesity and diabetes are considered together with their associated comorbidities

That obesity and diabetes can only be treated with insulin

That obesity and diabetes are no longer important disease areas

Q3. Why is scalability considered early in drug discovery?

Because all drugs are produced in the same amount regardless of indication

Because a drug that works biologically may still fail commercially if it cannot be produced at the required scale

Because scalability is only relevant after regulatory approval

Because rare disease treatments never require scale-up

Q4. Why can oral drugs require more compound than injectable drugs?

Because oral drugs always have higher bioavailability than injected drugs

Because oral drugs often have lower bioavailability, so more compound may be needed to achieve the same effect

Because oral drugs are never metabolized

Because oral drugs do not enter the bloodstream

Q5. Which statement best describes the role of clinical trials in drug development?

They are small laboratory tests with no logistical complexity

They mainly test whether a drug looks promising in a computer model

They are large, regulated studies involving patients, sites, vendors, data collection, and safety monitoring

They are optional if animal data are strong

Q6. What is the purpose of working backwards from the intended drug label or final claim?

To avoid doing experiments before approval

To define what evidence must be generated to support the desired clinical and regulatory claims

To make clinical trials unnecessary

To decide the drug price before discovering the molecule

Q7. What is a clinical candidate in the drug development process?

A patient selected for a clinical trial

A molecule or therapeutic concept judged promising enough to move toward clinical testing

A regulatory authority reviewing the drug

A completed medicine already on the market

Q8. What is the function of development 'gates' such as Gate 1, Gate 2, and Gate 3?

They are physical laboratory doors controlling access to experiments

They are decision points where evidence, risk, value, and investment are reviewed before progressing

They are only used after a drug reaches the market

They replace clinical trials

Q9. Why does the speaker repeatedly emphasize safety during development?

Because efficacy is irrelevant

Because safety must be assessed before and during human exposure, and regulators require strong safety evidence

Because animal studies can fully replace human safety studies

Because safety only matters after launch

Q10. Why are animal studies still used before first-in-human trials?

Because animal studies always perfectly predict human outcomes

Because regulatory authorities generally require preclinical safety testing before human exposure

Because human studies are never allowed

Because animal studies are used only for marketing

Q11. What is the main meaning of 'de-risking' in a drug development project?

Removing all uncertainty from a project

Generating evidence that reduces uncertainty and increases confidence that the project can succeed

Skipping expensive experiments

Choosing only drugs that are already approved

Q12. Why is Phase 3 especially expensive?

Because it is done only in test tubes

Because it often requires large patient populations, many sites, commercial-scale manufacturing readiness, and extensive regulatory-quality data

Because it happens before any candidate is selected

Because it never uses patients

Q13. What does 'white space' mean in the context of drug development timelines?

The empty area on a presentation slide

Time where no useful progress is happening, such as waiting between trial steps or before patients receive drug

The physical space inside a manufacturing tank

A type of regulatory document

Q14. Why might companies run multiple lead candidates in parallel?

To guarantee that all candidates will reach approval

To reduce timeline risk by keeping backup candidates available if the leading candidate fails

To avoid doing clinical studies

To make manufacturing unnecessary

Q15. True or false: The lecture presents obesity as only a problem of excess body weight, not as a driver of other diseases.

True

False

Q16. True or false: Rare disease drugs can be economically challenging because the patient population is small while development costs remain high.

True

False

Q17. True or false: The speaker suggests that GLP-1-based treatments were always obvious successes from the beginning.

True

False

Q18. True or false: A drug's route of administration can influence manufacturing requirements.

True

False

Q19. True or false: Drug development teams only need scientists; commercial, regulatory, manufacturing, and clinical specialists are not involved until after approval.

True

False

Q20. True or false: The intended final product label can influence early experimental planning.

True

False

Q21. True or false: Once a drug enters first-in-human testing, it has a very high probability of reaching the market.

True

False

Q22. True or false: Submission to authorities is usually the riskiest stage, with a lower success probability than early clinical development.

True

False

Q23. True or false: Manufacturing scale-up decisions can require large financial commitments before complete clinical certainty exists.

True

False

Q24. True or false: Clinical trial speed depends only on how fast scientists can design molecules.

True

False

Q25. True or false: Medical affairs are described as the people who can communicate scientific and clinical data to doctors.

True

False

Q26. True or false: Device development can be a major part of drug innovation.

True

False

Q27. True or false: A biologically effective drug automatically becomes a useful product, regardless of production capacity, stability, delivery, or market access.

True

False

Q28. True or false: Companies may acquire external technologies or biotech assets instead of developing everything internally.

True

False

Q29. True or false: Running development steps in parallel can save time but increases cost and risk.

True

False

Q30. True or false: The lecture presents drug development as a simple linear process where each step is completed with full certainty before the next begins.

True

False