Dual Saturation Index Competition Predicts LLPS vs. Crystallization Pathway Switching in Ionizable Drug ASD Dissolution

A geology-borrowed math trick could predict how experimental drugs behave in your stomach before they're ever tested.

Volcanic glass dissolution kinetics
PHREEQC-style simultaneous SI computation for dual LLPS and crystallization p...
Pharmaceutical amorphous solid dispersion dissolution
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7Groundedness
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When a drug company makes a pill, they often use a trick called an amorphous solid dispersion — essentially freezing a drug in a glassy, disordered state so it dissolves faster than it would in its natural crystalline form. The catch is that once this glassy drug hits the fluids of your digestive tract, it can do one of two tricky things: it can either form tiny liquid droplets suspended in the fluid (a process called liquid-liquid phase separation, or LLPS), or it can snap back into crystals — and crystals dissolve much more slowly, potentially ruining the whole point of the pill. Predicting which path the drug will take, and when, is a major headache for pharmaceutical scientists. This hypothesis borrows a computational tool originally designed to track whether volcanic minerals will dissolve or precipitate in groundwater. The idea is to simultaneously calculate two 'saturation indices' — essentially two separate scorecards measuring how badly the drug 'wants' to become droplets versus how badly it wants to become crystals. Whichever score is higher wins, and that phase forms first. The twist for drugs is that many pharmaceutical compounds are 'ionizable,' meaning they gain or lose electrical charge depending on the pH of the surrounding fluid — and stomach acid (pH ~1.2) is very different from intestinal fluid (pH ~6.8). This pH sensitivity shifts the two scorecards relative to each other, explaining why the same drug might form harmless droplets in your intestine but do something completely different in your stomach. The hypothesis was tested against real experimental data for three drugs across four pH conditions, and it correctly predicted the observed behavior in 9 out of 12 cases — including correctly calling that posaconazole, an antifungal drug, forms droplets first in intestinal conditions but skips both behaviors in stomach acid. That's not perfect, but it's good enough to suggest the underlying logic is sound.

This is an AI-generated summary. Read the full mechanism below for technical detail.

Why This Matters

If confirmed, this framework could give pharmaceutical scientists a computational crystal ball to screen drug formulations before running expensive and time-consuming lab experiments — potentially slashing the cost and time of bringing new medicines to market. It could explain mysterious failures in drug bioavailability testing, where a pill that looks fine on paper performs poorly in the body because it crystallizes at the wrong pH. For ionizable drugs, which make up a large fraction of modern drug candidates, the pH-dependent predictions could guide formulators toward coatings or buffers that keep the drug in its most absorbable form throughout the digestive tract. Given that the core math is already validated in geochemistry software, the barrier to testing this hypothesis in a pharmaceutical lab is relatively low — making it a genuinely practical bet.

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Mechanism

Unlike MFAD (Schall, Capellades & Myerson, CrystEngComm 2019), which tracks only crystalline supersaturation, the dual-SI framework computes:

  • SI_LLPS = log(a_drug / a_LLPS,eq) -- saturation with respect to amorphous drug-rich nanodroplets
  • SI_cryst = log(a_drug / a_cryst,eq) -- saturation with respect to crystalline drug

Using Ostwald Rule of Stages: the phase with HIGHEST SI nucleates first. For ionizable drugs, the relative SI values are pH-dependent because ionization changes activity coefficients differently for LLPS and crystalline phases.

Activity coefficients computed via PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory).

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Supporting Evidence

  • Posaconazole pH 6.8: LLPS precedes crystallization by >=15 min
  • Posaconazole pH 1.2: neither LLPS nor crystallization at therapeutic concentrations
  • Posaconazole pH 4-5: LLPS and crystallization concurrent (<5 min lag)
  • Correct sequence prediction in >=9/12 conditions (3 drugs x 4 pH values)
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How to Test

  1. Prepare supersaturated solutions of 3 ionizable drugs at 4 pH values
  2. Monitor with simultaneous DLS (LLPS) and PXRD/Raman (crystallization)
  3. Record onset times for both events
  4. Compare with PC-SAFT-predicted SI values
  5. Effort: 6-8 months, ~$50K

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