{"doi":"10.1007/s10928-024-09930-x","title":"Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice","abstract":null,"journal":"Journal of Pharmacokinetics and Pharmacodynamics","year":2024,"id":631324,"datarank":0.40620753016533157,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.0,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1636023,"name":"Claire Couty","orcid":null,"position":1,"is_corresponding":false},{"id":433764,"name":"Paul Lemarre","orcid":"0000-0001-9615-2188","position":2,"is_corresponding":false},{"id":1636024,"name":"Craig J. Thalhauser","orcid":null,"position":3,"is_corresponding":false},{"id":1230514,"name":"Yanguang Cao","orcid":"0000-0002-6870-0551","position":4,"is_corresponding":false},{"id":909998,"name":"Alexander Kulesza","orcid":"0000-0002-8812-8548","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice","abstract":"Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.","is_dataset_classified":null,"base_score":2.70805020110221,"endowment":2.70805020110221,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38904912","pmcid":"PMC11795844","openalex_id":"https://openalex.org/W4399891715","authors":[],"funders":[{"funder_name":"NIGMS NIH HHS","grant_id":"R35 GM152449","title":null},{"funder_name":"National Institutes of Health","grant_id":"1R35GM152449-01","title":"Quantitative Systems Pharmacology of Antibody Therapeutics across Diverse Biological Contexts"}],"total_grants":2,"fwci":4.115,"citation_percentile":0.94956315,"influential_citations":0,"citation_trend":[{"year":2024,"count":3},{"year":2025,"count":7},{"year":2026,"count":4}],"oa_status":"green","license":"Springer Nature TDM","oa_locations":[{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11795844/pdf/nihms-2045688.pdf","host_type":"repository"},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11795844/pdf/nihms-2045688.pdf","host_type":"repository"},{"url":"https://link.springer.com/content/pdf/10.1007/s10928-024-09930-x.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s10928-024-09930-x/fulltext.html","host_type":"publisher"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11795844","host_type":"repository"},{"url":"https://doi.org/10.1007/s10928-024-09930-x","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38904912","host_type":"repository"},{"url":"https://dx.doi.org/10.17615/m8s9-3e24","host_type":""}],"fields_of_study":["Mathematical Biology Tumor Growth","Gene Regulatory Network Analysis","Monoclonal and Polyclonal Antibodies Research","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":["Drug Development","Animals","Antineoplastic Agents","Humans","Models, Biological","Models, Theoretical","Neoplasms","Systems Biology"],"keywords":["Bridging (networking)","Computer science","Drug development","Management science","Data science","Computational model","Risk analysis (engineering)","Artificial intelligence","Drug","Medicine","Engineering","Oncology","Immuno-oncology","Tumor Growth Inhibition Models","Modeling & Simulation","Model Informed Drug Development","Ecologic And Evolutionary Modeling","Neoplasms","Systems Biology","Humans","Animals","Antineoplastic Agents","Models, Theoretical","Models, Biological"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T23:20:07.843859Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}