AI in Pharmaceutical Formulation: Why Excipient Data Consistency Matters

Artificial intelligence is progressively entering pharmaceutical formulation workflows, particularly in areas such as compatibility screening, dissolution profile prediction, formulation optimization and process modelling. These applications are promising, but their value depends strongly on the quality, consistency and traceability of the data used to train and validate the models [1-3].

For oral solid dosage forms, excipient data can be particularly important. A model may use information on particle size distribution, crystallinity, residual moisture, density or surface area to predict formulation behaviour.

This article focuses on one practical question for formulation teams: as artificial intelligence and machine learning become more common in pharmaceutical development. For pharmaceutical lactose suppliers, this question has implications that extend beyond digital tools. AI use emphasizes the need for consistency in both intra-batch and batch-to-batch production, as well as for analytical rigor and quality documentation.

AI in pharmaceutical formulation:
a developing but data-dependent field

Recent publications describe several applications of artificial intelligence in pharmaceutical technology and drug delivery design, including formulation screening, compatibility assessment and prediction of product performance [1,7]. Web-based tools such as Formulation AI have also been presented as examples of AI-supported formulation design platforms [2].

In practice, these tools are most relevant when the formulation space is well defined and when the training data are sufficiently robust. Artificial intelligence can help identify correlations, prioritize experiments and support Design of Experiments analysis. However, it does not remove the need for formulation expertise, experimental validation or regulatory justification.

Machine learning (ML) models are therefore best understood as decision-support tools. They may accelerate development work, but their predictions remain dependent on the quality and comparability of the input data. This is particularly relevant for excipients, because their material attributes can influence processing, tablet behaviour and dissolution-related parameters.

What AI can currently support in formulation development

Current documented uses of AI in formulation development are generally concentrated in specific tasks rather than in full autonomous formulation design. The most relevant areas include:

• API-excipient compatibility screening, using historical stability or compatibility data to identify combinations that may require further assessment [1,3].
• Dissolution profile prediction, where excipient attributes, API properties and process parameters may be correlated with measured dissolution behaviour [3].
• Formulation ratio optimization, often as an extension of classical Design of Experiments, regression modelling or machine learning approaches [2,3].
• Process outcome prediction, where formulation composition is linked to granulation, blending, flow or compression behaviour [3].

These applications can be useful, but they are generally limited to the design space and data quality available to the development team. A model trained on one set of excipient data may not automatically transfer to another supplier or another grade, even when the nominal specification appears similar. In this context, batch consistency within one batch and between delivered batches becomes an important condition for reliable modelling.

Why excipient data consistency matters

In formulation modelling, inconsistent input data can significantly limit the relevance of predictions. This is not specific to lactose, but it is particularly important for excipients used at relatively high levels in oral solid dosage forms, where material attributes may influence blend behaviour, compaction, disintegration and dissolution.

Several situations can reduce the value of an excipient dataset for modelling purposes:

• particle size data generated with different analytical methods, such as laser diffraction and sieve analysis, which are not comparable;
• crystallinity values measured under different humidity or sample preparation conditions;
• Brunauer-Emmett-Teller (BET) surface area data generated with different instruments or protocols;
• residual moisture values reported without sufficient information on method and conditions;
• limited historical data across production batches, which may make it difficult to distinguish normal variation from meaningful change.

When such differences are not enough monitored, the model may compensate by producing wider confidence intervals or less stable predictions. In practical terms, this can reduce the usefulness of AI-supported tools for formulation decision-making.

Pharmaceutical lactose: which parameters may be relevant for AI-supported models?

For pharmaceutical lactose used in oral solid dosage forms, several material attributes may be relevant to formulation models. Their importance depends on the dosage form, the process route, the API and the modelling objective.

These parameters are not automatically useful simply because they are measured. For modelling purposes, their value increases when they are measured with consistent analytical methods, documented across production batches and monitored within an appropriate specification framework.

From supplier comparison to batch consistency

For formulation teams using AI-supported models, data from one supplier or one grade should not be assumed to be interchangeable with data from another supplier or another grade. Two lactose grades may comply with the same pharmacopoeial monograph while still can differ in particle size distribution, morphology, density, crystallinity or residual moisture.

The gap between in silico prediction and industrial reality

Many AI-supported formulation models are initially developed with laboratory-scale data. Transfer to manufacturing scale can introduce additional variability, including blending time, granulation endpoint, compression force, equipment configuration and environmental conditions. These factors are not yet be fully represented in early datasets used by IA [3].

Regulatory agencies are also still building their frameworks for the use of AI and machine learning in drug development. The FDA published a discussion paper on AI and ML in drug and biological product development in 2023, and later publications describe continued cross-center work on AI and medical products [4,5]. In Europe, the HMA-EMA multi-annual AI workplan 2023-2028 sets out actions to guide the use of AI in medicines regulation [6].

This evolving framework reinforces the need for explainable, traceable and well-documented data. For formulation applications, black-box outputs may be difficult to justify if the underlying material data are incomplete or not comparable.

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Source: Lactalis Ingredients Pharma, website AI in Pharmaceutical Formulation: Why Excipient Data Consistency Matters – Lactalis Ingredients Pharma

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