Analytical applications of molecularly imprinted polymers: a personal view

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Yarman A., Oktay A., Sezer E., Akgol S., Ettner A., Ghanbari Z. S. Y., ...Daha Fazla

Analytical and Bioanalytical Chemistry, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Derleme
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s00216-026-06800-y
  • Dergi Adı: Analytical and Bioanalytical Chemistry
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Artic & Antarctic Regions, BIOSIS, Chemical Abstracts Core, Chimica, Compendex, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Artificial intelligence (AI), Biomimetic sensors, Epitope imprinting, Machine learning, Molecularly imprinted polymers
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Molecularly imprinted polymers (MIPs) are biomimetic counterparts of biomacromolecules that are synthesized by templating the target within a polymeric network. While the affinity of MIPs can compete with that of antibodies, the specific activity of natural enzymes remains superior to that of catalytically active MIPs. Although the mechanisms underlying the generation of antibodies and MIPs are fundamentally different, important principles have been transferred from biology to artificial binders. Templating a fragment of the target instead of the entire molecule or particle simplifies MIP synthesis and reduces costs (epitope imprinting), while hierarchical imprinting improves template orientation and generates more homogeneous binding sites than conventional random imprinting. The generation of anti-idiotypic MIPs by double imprinting follows the concept of anti-idiotypic antibodies, while post-imprinting modifications mimic post-translational modifications. Beyond classical terminal epitopes, recent approaches increasingly utilize internal epitopes, affinity tags, labels, carbohydrates, and surface motifs of viruses and cells as alternative imprinting targets. Recent advances in analytical MIP systems increasingly rely on the integration of nanomaterials to improve signal generation, mass transport, and target accessibility. In this context, nanoparticles, carbon-based nanomaterials, MXenes, and metal-organic frameworks (MOFs) have enabled the development of more efficient and functionally integrated sensing platforms. In parallel, computational rational design, artificial intelligence, and machine learning–assisted signal deconvolution are emerging as powerful tools for optimizing monomer selection, interpreting complex sensor outputs, and enabling multiplex analysis using cross-reactive MIP sensor arrays. Despite clear advantages such as higher stability, lower cost, and applicability to toxic or weakly immunogenic substances, the commercial success of MIPs is still restricted by challenges related to selectivity, reproducibility, and translation into robust real-world analytical platforms. This review discusses the evolution of analytical MIPs from classical molecular recognition materials toward increasingly intelligent biomimetic sensing systems.