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However, managing patients with complement\u2010related diseases is challenging owing to the different responses to treatments because of the heterogeneity of the aetiology of the different diseases, which may affect different proteins of the complement activation cascade. This study addresses these challenges using a comprehensive computational model, C\u2010model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Experimental Approach<\/jats:title><jats:p>C\u2010model is an enhanced pharmacokinetic\/pharmacodynamic simulation environment focused on the complement system, which can computationally model the alternative, classical and lectin activation pathways; the terminal\/lytic pathway; and their regulation in fluid phase and on erythrocytes and endothelial cells. It incorporates experimental data on patients and simulated drugs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Key Results<\/jats:title><jats:p>Our study demonstrates that C\u2010model is effective in forecasting complement biomarkers across healthy and diseased states, as well as their reaction to treatments. The simulations from this study are freely available for academic use at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/cmodel.pythonanywhere.com\">https:\/\/cmodel.pythonanywhere.com<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions and Implications<\/jats:title><jats:p>This extensive enhanced pharmacokinetic\/pharmacodynamic model supports the development of new therapies and personalised patient management by enabling scenario simulation and adaptation to various complement\u2010related diseases. 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