PhD Scientific Days 2026

Poster Session 1.F - Pharmaceutical Sciences and Health Technologies

A High-Throughput Platform for Solubility Classification to Support Early Drug Development

Előadó neve

Angi, Réka, PhD

Neptune code

QO02BR

Előadó munkahelye

Semmelweis University, Department of Pharmaceutical Chemistry

Előadó telefonszáma

06707708485

Előadó e-mail címe

angi.erzsebet@semmelweis.hu

Az előadás címe

A High-Throughput Platform for Solubility Classification to Support Early Drug Development

Szerző(k) neve és munkahelye

Réka Angi¹, Anna Vincze¹, Orsolya Basa-Dénes¹, Arash Mirzahosseini¹, Laura Szabadi², Dávis Havasi³, György Tibor Balogh¹

1: Semmelweis University, Department of Pharmaceutical Chemistry
2: Budapest University of Technology and Economics
3: mcule.com Kft.

Bemutatás módja

Poszter

Szekció

Poster Session 1.F - Pharmaceutical Sciences and Health Technologies

Language of the presentation

English

Preferred session

Pharmaceutical Sciences and Health Technologies

Összefoglaló szövege

Solubility is a critical operational parameter in modern automated synthesis and reaction design, and a key determinant of developability in pharmaceutical sciences; however, routine determination remains a bottleneck, as the classical “shake-flask” method is time- and resource-intensive. Here, we present a rapid, high-throughput (HTS) workflow for compound solubility classification across multiple solvents (methanol, acetonitrile, DMSO, dioxane, DMF, and water), focusing on practical solubility categories rather than exact thermodynamic values.
The method is implemented on a chemical-resistant, optically clear 96-well plate platform compatible with standard plate readers and requiring only 20–30 mg of material. Compounds are tested at predefined loading levels corresponding to nominal concentrations (10–200 mg·mL⁻¹). Solubility classification is achieved by combining two complementary optical readouts: nephelometry to detect turbidity and image analysis to identify residual solid material. The resulting signals are normalized and integrated into a quasi-quantitative scoring system, enabling assignment into discrete solubility categories.
To ensure robustness, blank plate analysis was applied for well-specific correction, and model performance was evaluated using cross-validation. Final compound-level classification was derived by mapping concentration-dependent responses onto physically consistent dissolution patterns. The method was further validated against miniaturized thermodynamic solubility measurements.
Overall, the developed workflow provides a fast, material-efficient, and automation-compatible platform for early-stage solubility screening and compound prioritization in drug discovery and synthetic chemistry.

University

Semmelweis University

Supervisor

György Tibor Balogh

Publication of my abstract

I do not give consent to the publication of my abstract on the website of the congress.

phd.section.field

after finishing doctoral studies with absolutorium (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

9706

Start

17:30

End

17:33