Week 4 — Oct 5: UV-Vis, Dissolution & Atomic Spectroscopy

(Lecture 4.) NMR read structure directly. This week uses the simplest measurement in the course — how much light a sample absorbs — for two very different jobs: quantitation (Beer’s law) and, via the dissolution test, performance — the fourth of Week 1’s four questions, is the drug available to the patient? It closes on atomic spectroscopy and elemental impurities.

The one idea

Beer’s law is the cleanest linear model in analytical chemistry: absorbance is proportional to concentration, full stop. Every deviation you will ever see is the physics or chemistry telling you that one of its assumptions has quietly stopped being true. Dissolution takes that same simple measurement and asks a harder question — not how much is there but how fast does it become available.

Beer–Lambert, and its fine print

A = ε b c — absorbance = molar absorptivity × path length × concentration. It holds when the light is monochromatic, the analyte is dilute and non-interacting, and nothing scatters or fluoresces. It fails — non-linearly, usually curving toward the concentration axis — when:

Teaching point: you keep absorbance in roughly 0.2–1.0 AU not by tradition but because that is where the assumptions are safest and the relative error is lowest.

UV-Vis in a regulated lab

Diode-array detection is the bridge to separations and to chemometrics: a full spectrum at each retention time is a data matrix, and peak-purity assessment is a small multivariate problem.

Dissolution — the performance test

Assay and impurity tests ask what is in the tablet. Dissolution asks what gets out of it, and how fast — a surrogate for the rate and extent of absorption. It is the one routine test that is about the patient’s experience rather than the molecule’s identity.

The apparatus (USP / Ph. Eur.):

What has to be controlled: the medium (volume, pH, surfactant, degassing), temperature (37 °C), agitation, and sink conditions — enough medium that the dissolved drug never approaches its saturation solubility, or the measured rate is limited by the medium, not the product.

Biorelevant and discriminating media. Simple buffers are used for QC; biorelevant media (FaSSIF/FeSSIF simulating fasted/fed intestinal fluid) are used in development to predict in-vivo behaviour. A good QC method is discriminating — it responds to the formulation and process changes that would matter clinically, and ignores the ones that wouldn’t. Building that discrimination is the hard part.

IVIVC. An in-vitro / in-vivo correlation links the dissolution profile to a pharmacokinetic profile. A validated Level A IVIVC can support a biowaiver — a formulation or site change approved on dissolution data instead of a new bioequivalence study. This is dissolution earning its keep: an in-vitro test standing in for a clinical one, the same trade the Q1 modernization and M7 (Q)SAR material describes elsewhere.

Acceptance criteria are staged (S1 → S2 → S3 in USP ⟨711⟩): test 6 units, then 6 more, then 12 more, with widening allowances — a built-in sampling design, not a single pass/fail.

Atomic spectroscopy — elemental impurities

Where UV-Vis measures molecules, atomic spectroscopy measures elements: atomise the sample, then measure absorption or emission at element-specific wavelengths (or count ions by mass).

The regulatory frame — ICH Q3D / USP ⟨232⟩–⟨233⟩. Q3D sets permitted daily exposures (PDEs) for elemental impurities by route of administration, in classes: Class 1 (As, Cd, Hg, Pb — always assessed), Class 2A (Co, V, Ni — likely), Class 2B and Class 3 (assessed if intentionally added or a known risk). The analyst’s job splits in two:

• The risk assessment — where could each element come from (drug-substance synthesis and catalysts, excipients, water, manufacturing equipment, container closure), and does the total plausibly approach the PDE? This is a Q9 exercise, and it decides whether routine testing is even needed.
• The method — closed-vessel microwave digestion, then ICP-OES or ICP-MS, validated per USP ⟨233⟩ (specificity, accuracy by spiked recovery, precision, a demonstrated limit) with internal standards and often standard addition for matrix effects. Interferences to teach: spectral overlap, matrix-induced signal suppression, ionization interference, and polyatomic interferences in ICP-MS (managed with a collision/reaction cell).

Worked case — from “heavy metals” to element-specific testing

Until the 2010s, most pharmacopeias controlled elemental impurities with a single colorimetric heavy-metals test (old USP ⟨231⟩): precipitate metal sulfides, compare the resulting brown colour to a lead standard, report pass/fail. It was cheap and universal — and wrong in both directions. It under-recovered the elements of most concern (mercury, and much of the arsenic and lead, were lost in sample preparation) and it flagged samples on colour that had nothing to do with toxic metals.

ICH Q3D and USP ⟨232⟩/⟨233⟩ replaced it with a risk assessment plus element-specific, validated instrumental methods. The lesson is specificity: a test that produces a number (or a pass) is worthless if the number isn’t a measurement of the thing you care about. The colorimetric test had good precision and terrible accuracy for the actual question.

Risk-assessment assignment

Using the FMEA / risk-ranking approach from Week 2: take a drug product of your choice and build the Q3D elemental-impurity risk assessment — sources, contributions, and a defended conclusion on which elements (if any) need routine testing and at what stage. Score the detection honestly: for each element, is there a validated method in place that could see it at 30% of the PDE?

Where the analyst sits

Beer’s law makes UV-Vis feel automatic; the dissolution monograph makes performance feel like a recipe; Q3D makes elemental impurities feel like a checklist. Each hides judgment. Is that curvature stray light or a chemical equilibrium? Is this dissolution method actually discriminating, or does it pass every batch including the ones that would fail in a patient? Does the elemental-impurity method really detect what the risk assessment assumes it can? The STEAM “A” is deciding when “the number is fine” is actually true. The refrain: science → evidence → reduced uncertainty → control → regulatory confidence → patient trust.

For discussion

• Your assay reads 1.8 AU and the calibration curve is slightly concave. List the causes in the order you would rule them out, and how.
• A dissolution method passes every batch you have ever made. Is that good news? How would you find out whether it discriminates?
• “Sink conditions” — why does violating them make a dissolution result meaningless, and how would you detect that you had?
• A Level A IVIVC lets you replace a bioequivalence study with a dissolution profile. What has to be true about the method for a regulator to accept that trade?
• The old colorimetric heavy-metals test had a %RSD better than many ICP methods. Why is that not reassuring?
• ICP-MS can measure lead at parts-per-trillion. When does that sensitivity become a liability rather than an asset?

Source note. Beer–Lambert deviations follow standard instrumental-analysis texts (Skoog; Harris). UV-Vis: USP ⟨857⟩, Ph. Eur. 2.2.25. Dissolution: USP ⟨711⟩ / ⟨724⟩ / ⟨1092⟩, Ph. Eur. 2.9.3, the FDA dissolution and BCS-biowaiver guidances, and the IVIVC guidance. Elemental impurities: ICH Q3D(R2), USP ⟨232⟩/⟨233⟩, and the history of the withdrawn ⟨231⟩; risk assessment connects to Q9 and Q3. (Instructor: confirm current Q3D revision and the dissolution/biowaiver guidance status; set the risk-assignment product so it isn’t the Week 2 nitrosamine example; decide how much biorelevant-media detail belongs here versus the modalities weeks.)

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