Increasing data on the infection indicate that maternal infections are severe. Under the realms of vaccine development, virus‐like particles (VLP)/nanoparticles (NPs) hold the promise of targeted control of therapeutics transfer across the placental barrier with the potential to trigger innate immune responses. Though the placenta is known to act as a barrier against exogenous materials, viruses exploit the transport systems and overcome the barrier properties. VLPs can be strategically designed to obtain the necessary mechanisms for navigation across the placenta and immune response. However, several knowledge gaps on the chemical, viral transmission strategies and the host defense response exist owing to the highly dynamic etiology of the placental barrier. This further complicates the toxicological analysis of the developed therapeutics. Herein, placental physiology and functions are discussed in significance with chemical toxicology, viral infections, and the host defense. Further, the promising applications of VLPs and perspective on their design to overcome the placental gatekeeper to gain the necessary immune response or therapy are provided. Finally, a holistic approach to various bioengineering models for studying chemical toxicants, viral infections, and effects of VLPs is provided to facilitate better translation of these VLPs to clinical applications.

Owing to limited drug testing possibilities in pregnant population, the development of computational algorithms is crucial to predict the fate of drugs in the placental barrier; it could serve as an alternative to animal testing. The ability of a molecule to effectively cross the placental barrier and reach the fetus determines the drug's toxicological effects on the fetus. In this regard, our study aims to predict the permeability of molecules across the placental barrier. Based on publicly available datasets, several machine learning models are comprehensively analysed across different fingerprints and toolkits to find the best suitable models. Several dataset analysis models are utilised to study the data diversity. Further, this study demonstrates the application of neural network-based models to effectively predict the permeability. K-nearest neighbour (KNN), standard vector classifier (SVC) and Multi-layer perceptron (MLP) are found to be the best-performing models with a prediction percentage of 82%, 86.4% and 90.8%, respectively. Different models are compared to predict the chosen set of drugs, drugs like Aliskiren, some insulin secretagogues and glucocorticoids are found to be negative while predicting the permeability.INDEX TERMS Placenta barrier, machine learning, drug permeability, developmental toxicity.

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