2026 Lecture

This is the 2026 course, organised weeek by week to match the department’s actual syllabus. Each week has its own folder in the left menu.

What you’ll be able to do

By the end of the term, you should be able to:

• Defend a method, not just run it. For any validated analytical method, state what evidence supports it, name its most likely failure modes, and say what result would make you distrust it.
• Build and use a risk assessment. Construct a method or process FMEA, score severity, occurrence, and detection without inflating detection to look reassuring, and turn the assessment into a control strategy a regulator could follow (Week 2, and the risk-homework checkpoints in Weeks 5, 6, and 10).
• Match the technique to the question. Given an analytical problem — an elemental impurity, an unknown degradant, a charge-variant shift, a polymorph question — choose and justify the right technique among NMR, atomic and molecular spectroscopy, chromatography, solid-state/thermal characterization, flow cytometry, and mass spectrometry, and state what that technique can’t tell you (Weeks 3–10).
• Read real instrumental data. Interpret an NMR spectrum, a UV-Vis/IR/Raman trace, a chromatogram, or a mass spectrum well enough to identify a structure, flag a co-elution or ion-suppression artifact, or catch a spectral match that looks right but isn’t.
• Trace a decision from molecule to specification. Explain how what’s being made — small molecule, biologic, or advanced therapy — changes which failure modes matter and how “identity” or “purity” is even defined, and read a Certificate of Analysis line by line back to the method and manufacturing step behind it.
• Explain how a control strategy is built, held, and reopened. Describe the at-/on-/in-line measurement hierarchy and real-time release testing, and how a complaint or pharmacovigilance signal can force a control strategy that passed every existing test to be revisited (Week 11).
• Judge whether a computational or AI-based result can be trusted. Distinguish predictive/chemometric models from generative AI by how each is governed, recognise information leakage or an out-of-domain prediction, and state what evidence a model needs before it can support a regulatory decision (Week 12).
• Make and defend an evidence-based analytical argument in writing, naming a method’s failure modes and their detectability and connecting the technique to a regulatory expectation and a patient consequence (final paper and capstone).

One argument, taught by three instructors

The course is not a tour of instruments. It is a single argument — a measurement is a claim that has to earn trust before it can move a product or a patient — and it’s taught by three people: MAI, SM, and AF. AF and SM together lead nine of the term’s lecture sessions; MAI leads two (NMR); the rest are the mid-term, the final, and the occasional make-up.

1. The ground (Weeks 1–2). Why analysis is a science, what it is for inside a company that has to discover, develop, and sell a medicine, the regulations that govern every analytical decision, how a method actually gets developed and regulated, what’s actually being made across small molecule / large molecule / advanced therapy, and the risk thinking that decides how much evidence is enough.
2. NMR (Weeks 3–4, MAI). Introduction to NMR spectroscopy, then interpretation — the one part of the term AF and SM hand off entirely.
3. The AF/SM technique arc (Weeks 5–6, 8–10). Atomic spectroscopy and molecular spectroscopy (with UV-Vis and a Certificate-of-Analysis read-through) are taught on the small molecule alone; separation methods and mass spectrometry each add an applied case extending the same technique to biologics and advanced therapies. In between, Week 9 pivots to the specialized and solid-state techniques that don’t fit that main arc but are load-bearing in a real QC lab — DSC/TGA, X-ray powder diffraction and crystallography, flow cytometry, and dissolution. A risk-homework thread runs through the atomic-spectroscopy, molecular-spectroscopy, and mass-spectrometry weeks.
4. Control and the data layer (Weeks 11–12). Process analytical technology and automation — how a control strategy is built and held once a product is made for real — then chemometrics, miscellaneous methods (AI and the regulatory landscape), and final paper presentations.

The mid-term falls after the first technique pair; the final is cumulative, with Week 14 held only if a make-up session is absolutely necessary. Every week returns to the same refrain: science → evidence → reduced uncertainty → control → regulatory confidence → patient trust.

The weeks at a glance

Week 1

Week 1 — Sep 14 is the opening arc in full, and it is substantial: it moves deliberately in one direction — science → pharma → regs — then reframes clinical development as the progressive removal of uncertainty and turns to the working detail: quality control across the supply chain, lab automation and process analytical technology, knowledge management, and the cost of development. It is the whole course in miniature; the weeks that follow slow down and do the work.

Work through each week’s sections in order; use the “On this page” list on the right to move within a section, and the ← / → buttons at the foot of each page to step through the course in sequence.

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