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1Kamla Nehru College of Pharmacy, Butibori, (RTMNU)
2Shri Sant Gajanan Maharaj College of Pharmacy, Bhuldana
3Dr. Rajendra Gode, Institute of Pharmacy, Amravati
4Vidyaniketan College of Pharmacy, Anjangaon Surji, Dist. Amravati, Maharashtra 444705.
5Dr. Rajendra Gode, College of Pharmacy, Malkapur
Stability testing determines whether a pharmaceutical product keeps its identity, strength, quality and purity over its labeled shelf life, but conventional studies are slow, expensive and material-intensive, and they offer limited insight into why a formulation degrades. Artificial intelligence (AI), and machine learning (ML) in particular, is increasingly used to predict degradation pathways, physical stability, and time to specification failure from formulation, process, structural and early stability data. This review summarizes the scientific and regulatory background of stability assessment; the main AI methods applied (tree ensembles, kernel methods, Gaussian processes, deep and graph neural networks, and hybrid mechanistic-ML models); data requirements and feature engineering; applications to small molecules, amorphous solid dispersions, biologics, nucleic acids and drug-delivery systems; and an end-to-end workflow for AI-assisted shelf-life estimation. We discuss validation, uncertainty quantification, explainability and regulatory expectations, and outline the challenges that must be solved before AI-derived predictions can support labeled shelf-life claims. Current evidence suggests that AI is best positioned today as a decision-support tool for formulation screening, study design and risk assessment, complementing rather than replacing real-time stability data.
Shelf life is the period during which a drug product is expected to remain within approved specifications when stored under labeled conditions. It is established from stability studies designed under the International Council for Harmonisation (ICH) Q1A(R2) guideline and evaluated statistically under ICH Q1E [1,2]. Long-term studies commonly run for 12 months or more before submission and continue for the proposed shelf life, so stability data often arrive late in development, when reformulation is costly. Quality by design (QbD) and quality risk management ask developers to understand how formulation and process variables affect critical quality attributes, including stability [3-5]. Accelerated approaches such as the Accelerated Stability Assessment Program (ASAP) use short studies at elevated temperature and humidity, fitted with humidity-corrected Arrhenius models, to rank formulations and forecast long-term behavior of many solid dosage forms [6-8]. These methods work well when degradation follows a single dominant, well-behaved mechanism, but they are less reliable for complex, multi-mechanism or non-Arrhenius behavior. In parallel, AI has become established across drug discovery, formulation and delivery [9-11]. The same techniques can learn structure-stability relationships from historical and experimental data, potentially shortening development timelines and reducing the number of experiments. This review examines how AI is being applied to stability prediction and shelf-life estimation, what evidence supports it, and what limits its regulatory use today.
2. Conventional Stability Science and Its Limits
Chemical degradation in pharmaceuticals is classically described by reaction kinetics, with temperature dependence following the Arrhenius equation and additional dependence on humidity, pH, oxygen, light and excipient interactions [12,13]. Forced degradation (stress testing) under heat, humidity, acid, base, oxidation and light identifies likely degradation pathways and supports development of stability-indicating methods [14,15]. Biologics add further layers of complexity, including aggregation, deamidation, oxidation, fragmentation and interfacial damage [16,17]. The ICH framework prescribes standard storage conditions, summarized in Table 1.
Table 1. Standard stability study conditions for the general case under ICH Q1A(R2) [1].
|
Study |
Storage condition |
Minimum duration at submission |
|
Long-term |
25 °C ± 2 °C / 60% RH ± 5% (or 30 °C / 65% RH) |
12 months |
|
Intermediate |
30 °C ± 2 °C / 65% RH ± 5% |
6 months |
|
Accelerated |
40 °C ± 2 °C / 75% RH ± 5% |
6 months |
Conventional approaches face several recurring limitations:
3. Artificial Intelligence Methods Used for Stability Prediction
3.1 Classical machine learning
Tree ensembles such as random forests and gradient-boosted trees handle mixed numeric and categorical inputs, tolerate missing data and perform well on the small to medium tabular datasets typical of formulation work [18,20]. Support vector machines are effective for classification tasks such as stable versus unstable, particularly with limited samples [19]. Gaussian processes provide predictions with built-in uncertainty estimates and suit data-scarce settings and sequential experimental design [21].
3.2 Deep learning
Deep neural networks learn hierarchical representations from large datasets [22]. Recurrent architectures such as long short-term memory networks model stability time series, for example degradant growth across pull points [23]. Graph neural networks operate directly on molecular graphs and learn degradation-relevant substructures without hand-crafted descriptors [24,25]. Deep learning typically requires larger datasets than are available for a single product, so it is most valuable when trained on pooled or public data, or pretrained and fine-tuned.
3.3 Hybrid and physics-informed models
Physics-informed neural networks and neural ordinary differential equations embed kinetic laws into the learning problem, so predictions respect mass balance and known rate behavior while the network learns the unknown terms [26,27]. For stability, this suggests learning how rate constants depend on formulation variables while keeping an Arrhenius or humidity-modified kinetic backbone, an approach that extends ASAP-style modeling.
3.4 Uncertainty, optimization and transfer learning
Shelf-life decisions depend on how confident a prediction is, not only its value. Bayesian deep learning and dropout-based approximations provide uncertainty estimates for neural models [28], and Bayesian optimization uses them to choose the next informative experiment [29]. Transfer learning reuses knowledge from related compounds or data-rich tasks to improve prediction on small datasets [30].
3.5 Explainability
Model-agnostic explanation tools such as SHAP and LIME attribute predictions to input features and help scientists check whether a model relies on chemically sensible drivers such as moisture content, reactive functional groups or excipient impurities [31,32]. Others argue that for high-stakes decisions inherently interpretable models should be preferred over post hoc explanations of black boxes [33].
Table 2. Common AI approaches for stability prediction.
|
Approach |
Typical stability use |
Strength |
Limitation |
Ref. |
|
Random forest, gradient boosting |
Degradant level or stability class from formulation variables |
Robust on small tabular data; feature importance |
Limited extrapolation outside training range |
[18,20] |
|
Support vector machine |
Stable versus unstable classification |
Good with few samples |
Sensitive to kernel and scaling; no native uncertainty |
[19] |
|
Gaussian process |
Small-data regression; experiment selection |
Calibrated uncertainty |
Scales poorly to very large datasets |
[21] |
|
Deep and recurrent networks |
Time-series degradation profiles |
Learns complex nonlinear patterns |
Data-hungry; opaque |
[22,23] |
|
Graph neural networks |
Molecule-level degradation liability |
No manual descriptors |
Needs large, diverse training sets |
[24,25] |
|
Physics-informed / neural ODE |
Kinetics with learned parameters |
Respects known kinetics; better extrapolation |
More complex to build and validate |
[26,27] |
4. Data Sources and Feature Engineering
Model quality is limited by data quality. Typical inputs fall into five groups:
Stability data are frequently scattered across reports, instruments and sites. Applying the FAIR principles (findable, accessible, interoperable, reusable) to stability data, with harmonized units, metadata and ontologies, is a prerequisite for pooled modeling [36]. Because degradation endpoints are often censored (below the limit of quantitation) and unbalanced across conditions, careful handling of censoring, replicate structure and batch effects is essential.
5. Applications
5.1 Chemical degradation of small molecules
Knowledge-based software such as Zeneth predicts likely degradants from structure and conditions, and benchmarking against forced-degradation outcomes has shown useful but imperfect agreement [37]. Data-driven reaction-outcome models, trained on large reaction corpora, offer a complementary route to anticipating transformations such as oxidation and hydrolysis [38]. Combining these tools with experimental stress data can prioritize which degradants to monitor.
5.2 Physical stability and amorphous solid dispersions
Crystallization of amorphous drugs undermines solubility advantages. ML models trained on solid-dispersion datasets have been used to predict physical stability from drug properties, polymer type, drug loading and storage conditions [39]. Classification of crystallization tendency from molecular properties and thermal behavior provides a basis for feature selection [40,41].
5.3 Biologics and developability
For therapeutic antibodies, developability assessment uses biophysical and sequence-derived features to flag aggregation- and viscosity-prone candidates early. Landscape analyses of clinical-stage antibodies defined property ranges that inform such models [42], and computational guidelines operationalize them for screening [43]. Sequence-based tools predict solubility and aggregation propensity, linking molecular design to stability outcomes [44].
5.4 Nucleic-acid products
RNA instability limits the storage of mRNA vaccines and therapeutics. A crowdsourced dataset and deep learning models predicted degradation along RNA sequences, showing that sequence and structure can be optimized for stability [45]. Similar approaches may inform design of lipid-nanoparticle formulations.
5.5 Formulation design and delivery systems
ML models are being used to design formulations that meet performance and stability targets. Examples include models that accelerate the design of polymeric long-acting injectables [46] and deep-learning models that predict in vitro properties of oral and other formulations [47]. In these settings, stability-related endpoints become one of several objectives optimized jointly.
5.6 From degradation rate to shelf-life estimate
Shelf-life assignment under ICH Q1E relies on regression of attribute versus time and the time at which the one-sided 95% confidence limit of the mean intersects the specification limit [2]. AI can enter at three points: predicting initial rate parameters from formulation data before real-time results exist, pooling batches or related formulations to narrow intervals, and flagging atypical batches. Any AI-derived estimate should be reported with an uncertainty interval and confirmed by real-time data before it supports a labeled claim.
Table 3. Representative AI applications in stability and related areas.
|
Application |
Typical inputs |
Typical output |
Ref. |
|
Degradant prediction |
Structure, conditions |
Likely degradation products |
[37,38] |
|
Accelerated shelf-life modeling |
Short-term temperature and humidity data |
Degradation rate and time to limit |
[6-8] |
|
Amorphous dispersion physical stability |
Drug, polymer, loading, storage |
Stable or crystallizing |
[39-41] |
|
Antibody developability |
Sequence and biophysical properties |
Aggregation or viscosity risk |
[42-44] |
|
mRNA degradation |
Sequence and structure |
Per-nucleotide degradation |
[45] |
|
Formulation design |
Composition and process variables |
Predicted performance and stability |
[46,47] |
6. An End-to-End Workflow for AI-Assisted Shelf-Life Estimation
A practical workflow links data, models and decisions so that each prediction is traceable:
7. Validation and Regulatory Considerations
Current guidelines do not exclude model-based approaches: ICH Q8 and Q9 encourage science- and risk-based development, and QbD explicitly values mechanistic understanding [3-5]. For regulatory use, the U.S. Food and Drug Administration has proposed a risk-based credibility framework for AI used to support regulatory decisions on drugs and biologics, built around the model's context of use [48], and the European Medicines Agency has published a reflection paper on AI across the medicinal product lifecycle [49]. For shelf-life claims, the established expectation remains real-time data evaluated under ICH Q1E; AI predictions are therefore most defensible when used to support study design, justify bracketing or matrixing, prioritize risks, and provide supportive evidence, with clear context of use, validation and human oversight.
Key validation elements include independent test sets from different formulations or batches, prospective confirmation, evaluation of applicability domain, sensitivity analysis, and change control for model updates.
8. Challenges
9. FUTURE PERSPECTIVES
Likely developments include federated learning across companies to pool data without sharing proprietary formulations, hybrid models that combine kinetics with learned corrections, active-learning loops that choose the next stability experiment, digital twins of storage and supply-chain conditions, and tighter coupling of in-line sensing with predictive models. Standardized benchmark datasets and shared reporting formats would accelerate progress and permit fair comparison between methods.
CONCLUSION
AI offers a promising route to faster, more informative stability assessment by learning from structure, formulation and early stability data. Current successes are strongest in screening, risk ranking, and physical and biologic stability prediction, with hybrid mechanistic-ML approaches particularly attractive for shelf-life estimation. Realizing regulatory-grade use requires high-quality pooled data, rigorous validation, uncertainty quantification, interpretability and prospective confirmation against real-time data. Used in this way, AI can shorten development timelines and reduce experimental burden while retaining the evidentiary standard on which shelf-life claims depend.
REFERENCES
Tanmay Giradkar*, Rohan Rathod, Shejal Kadu, Reshma Rajput, Purva Dod, Janhavi Choukhnade, Vivek Daberao, Application of Artificial Intelligence in Stability Prediction and Shelf-Life Estimation of Pharmaceutical Formulations, Int. J. Med. Pharm. Sci., 2026, 2 (11), 11-18. https://doi.org/10.5281/zenodo.23938132
10.5281/zenodo.23938132