ArticleMolecular pharmaceutics2025
Bayesian Optimization for Efficient Multiobjective Formulation Development of Biologics.
Article in Molecular pharmaceutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Article
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- Quality by Design to Mitigate Aggregation: Mechanistic Insights and Analytical Strategies for Biopharmaceutical Manufacturing.Therapeutic innovation & regulatory science · 2026Review
- From Polyphenols to Prodrugs: Bridging the Blood-Brain Barrier with Nanomedicine and Neurotherapeutics.International journal of molecular sciences · 2026Review
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
- Artificial Intelligence (AI) in Pharmaceutical Formulation and Dosage Calculations.Pharmaceutics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biologics, including emerging engineered formats, can often exhibit poor developability profiles, complicating their translation into successful therapeutics. While formulation design can substantially mitigate some developability issues, it represents a highly complex optimization challenge due to the need to simultaneously improve multiple biophysical properties, navigate a vast design space, and account for nonlinear or synergistic interactions among excipients. Traditional design of experiments methods can reduce experimental effort but are limited by difficulties in managing high-order complexities and a propensity to become trapped in local optima. In response, machine learning techniques combined with (high-throughput) screenings have emerged as powerful strategies to overcome these limitations, dramatically reducing the number of required experiments. The ability of these models to capture nonlinear relationships and interactions among multiple features enables efficient navigation in a high-dimensional design space. We present a combined Bayesian optimization and experimental screening method that concurrently optimizes three key biophysical properties of a monoclonal antibody─melting temperature
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.