Aamir Shahzad

Baba Guru Nanak University, Pakistan

Title: Exploring synergistic therapeutic potential of Erlotinib and artemisinin in non-small cell lung Cancer (NSCLC) using pharmacological networking and mathematical modeling

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Biography

Dr. Aamir Shahzad is a mathematician, educator, and researcher specializing in computational mathematics. He earned his PhD from the University of Sargodha, where his research focused on subdivision curves and surfaces. He currently serves as a Lecturer at Baba Guru Nanak University and holds several academic and administrative responsibilities within the Department of Mathematics. With extensive teaching experience at undergraduate and postgraduate levels, he has taught subjects including numerical analysis, mathematical modeling, data science, and machine learning. Dr. Shahzad has published numerous research papers, presented at national and international conferences, and contributed as a journal reviewer and academic resource person.

Abstract

Lung cancer remains one of the most aggressive malignancies worldwide, with non-small cell lung cancer (NSCLC) constituting the major subtype. Resistance to targeted therapies poses a persistent challenge in precision medicine, emphasizing the need for more effective therapeutic strategies.

This study explored the synergistic potential between Erlotinib (ERL) and Artemisinin (ART), a phytochemical compound, using network pharmacology, differential gene expression (DGE) analysis, and mathematical models of potential drug synergy and conceptual interaction modeling. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to identify potential key molecular targets and biological pathways involved in NSCLC treatment response.

Key potential targets such as HSP90AA1, SRC, ABL1, and JAK2 were identified, with significant modulation of the PI3K-Akt, Rap1, Ras, and VEGF signaling pathways contributing to therapeutic outcomes. Exploratory synergy modeling using the Bliss, Loewe, and simplified ZIP models revealed potential synergistic trends between Erlotinib and Artemisinin, indicating enhanced anti-tumor potential compared to Erlotinib monotherapy. Mathematical evaluation of drug conceptual interaction modeling provided further insights into Predicted drug–target interactions and pathway-level complementarity.

This integrative computational and mathematical approach elucidates the putative mechanistic interplay between drugs and phytochemicals, highlighting the potential of Artemisinin–Erlotinib (ART-ERL) combination therapy in overcoming drug resistance in NSCLC. The findings offer a promising framework for rational design of future combination strategies to improve clinical outcomes in lung cancer therapy.