Bandingkan kaedah

	Ujian Kebolehtelapan Sel Caco-2	Elektrofisiologi Patch-Clamp	Pemodelan Farmakodinamik Populasi
Bidang	Farmakologi	Farmakologi	Farmakologi
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1989	1976	1992
Pengasas	Ingrid Hidalgo	Erwin Neher and Bert Sakmann	Lewis Sheiner and Stephen Roush
Jenis	absorption screening	ion channel screening	dose-response modeling
Sumber perintis	Hidalgo, I. J., Raub, T. J., & Borchardt, R. T. (1989). Characterization of the human colon carcinoma cell line (Caco-2) as a model system for intestinal epithelial permeability. Gastroenterology, 96(3), 736-749.	Neher, E., & Sakmann, B. (1976). Single-channel currents recorded from membrane of denervated frog muscle fibres. Nature, 260(5554), 799-802.	Dahlström, B., & Nyberg, L. (1993). Population pharmacokinetics and pharmacodynamics. Clinical Pharmacokinetics, 24(1), 45-57.
Alias	Caco-2 assay, intestinal permeability, ADME screening	patch clamp, whole-cell recording, ion channel assay	PopPD, population PD, hierarchical PD modeling
Berkaitan	3	3	3
Ringkasan	The Caco-2 assay is an in vitro model system using human colon carcinoma cell monolayers to screen drug intestinal permeability. Developed by Hidalgo and colleagues in 1989, Caco-2 cells differentiate into an epithelial barrier resembling intestinal mucosa, enabling rapid assessment of drug absorption potential and identification of transporter-mediated transport.	Patch-clamp electrophysiology is a technique for measuring ionic currents through ion channels in cell membranes, developed by Neher and Sakmann in 1976. It enables direct observation of single-channel and whole-cell currents at millisecond resolution, making it essential for characterizing drug effects on ion channels and cardiac safety assessment.	Population pharmacodynamic (PopPD) modeling integrates pharmacokinetics with individual dose-response relationships across patient populations to characterize drug efficacy and tolerability. Pioneered by Lewis Sheiner and colleagues, PopPD accounts for inter-individual variability in drug effects and enables rational dose optimization and response prediction.