C: Cardiovascular system

ATC group C covers the heart and blood vessels: drugs for heart failure and arrhythmias, blood pressure drugs, diuretics, and cholesterol lowering drugs.
Updated

September 25, 2026

Group C holds the drugs that act on the heart and the blood vessels, together with the drugs that lower cholesterol and other lipids in the blood.

Table 1: ATC C level 2 codes
Description In plain terms Example drug
C01 Cardiac therapy drugs acting directly on the heart Amiodarone (C01BD01) / digoxin (C01AA05)
C02 Antihypertensives Methyldopa (C02AB01), doxazosin (C02CA04). Most other blood pressure drugs sit in C03, C07, C08 and C09.
C03 Diuretics Furosemide (C03CA01) / spironolactone (C03DA01)
C04 Peripheral vasodilators widen arteries in the arms and legs Naftidrofuryl (C04AX21), pentoxifylline (C04AD03).
C05 Vasoprotectives for hemorrhoids, varicose veins, fragile capillaries Topical hydrocortisone (C05AA01) for hemorrhoids; diosmin (C05CA03), a capillary stabilizing agent.
C07 Beta blocking agents Bisoprolol (C07AB07) / metoprolol (C07AB02)
C08 Calcium channel blockers Amlodipine (C08CA01) / diltiazem (C08DB01)
C09 Agents acting on the renin-angiotensin system blood-pressure hormone system: angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs) Ramipril (C09AA05) / candesartan (C09CA06)
C10 Lipid modifying agents cholesterol- and triglyceride-lowering drugs Atorvastatin (C10AA05) / rosuvastatin (C10AA07)

Blood pressure

For the antihypertensives in C02, C03, C07, C08 and C09, efficacy is measured as the fall in blood pressure [1]. Cardiovascular outcomes, such as stroke and heart attack (myocardial infarction), are clinical events, analyzed as time to event [2].

Blood pressure is a continuous measurement, and a monitor worn during daily life (ambulatory monitor) can repeat it many times over 24 hours. In a population analysis of moxonidine (C02AC05), the 24-hour profiles of one of its two studies held 74 to 99 measurements each. Two cosine terms described the circadian rhythm in the baseline, and this baseline model was combined with an Emax model for the drug effect, with an effect compartment for the delay [3].

In patients with heart failure given moxonidine, the effects on systolic blood pressure and heart rate lagged behind the plasma concentration, and were also described with an effect compartment. The concentration giving half the maximal effect (EC50) rose over the 12 weeks of treatment [4].

Francheteau and colleagues built a closed-loop model of cardiovascular regulation, in which a control variable representing mainly the pressure-sensing reflex of the arteries (baroreceptor reflex) responds to changes in mean arterial pressure. In it, the calcium channel blocker nicardipine (C08CA04) lowered the resistance of the blood vessels to flow (total peripheral resistance). Together with the feedback loop, this described the time course of both the blood flow pumped by the heart (cardiac output) and mean arterial pressure after intravenous infusion in healthy volunteers [5].

Baseline and placebo

In 255 subjects given placebo or one of five types of antihypertensive drug, the fall in systolic blood pressure correlated with the initial blood pressure in every group. In the subset analyzed with concentration-effect models, the sensitivity to the drug also correlated with the initial blood pressure [6].

Across the placebo arms of 52 trials in non-resistant hypertension, systolic blood pressure fell by 5.9 mm Hg. The fall was larger in trials with a higher baseline blood pressure [7].

Dose finding

Several antihypertensive drugs were introduced at doses that were too high, among them most thiazide diuretics, propranolol (C07AA05), atenolol (C07AB03) and captopril (C09AA01). These doses usually came from studies that raised the dose at regular intervals until the desired effect or unacceptable adverse effects appeared. The starting doses were often too high, and the intervals between dose steps too short. In many cases the high doses caused unnecessary adverse effects, such as the biochemical effects of thiazide diuretics, and taste disturbances and a shortage of neutrophil white blood cells (neutropenia) with captopril. Johnston recommended parallel-group designs with single doses, and with enough statistical power to separate the upper and lower ends of the dose-response curve [1]. Good practices for exposure-response analyses of clinical endpoints, which support such dose decisions, are set out in a tutorial [8].

Covariates

For furosemide (C03CA01), a review compares pharmacokinetics and pharmacodynamics in health and in a range of diseases [9]. When covariates are selected stepwise and only the statistically significant ones are kept, the covariate model risks selection bias, and correlated covariates add to it and cost power [10].

References

[1]
Johnston GD. Dose-response relationships with antihypertensive drugs. Pharmacology & Therapeutics 1992;55:53–93. https://doi.org/10.1016/0163-7258(92)90029-Y.
[2]
Holford N. A Time to Event Tutorial for Pharmacometricians. CPT Pharmacometrics Syst Pharmacol 2013;2:e43. https://doi.org/10.1038/psp.2013.18.
[3]
Hempel G, Karlsson MO, Alwis DP de, Toublanc N, McNay J, Schaefer H-G. Population pharmacokinetic-pharmacodynamic modeling of moxonidine using 24-hour ambulatory blood pressure measurements. Clinical Pharmacology and Therapeutics 1998. https://doi.org/10.1016/S0009-9236(98)90053-4.
[4]
Brynne L, McNay JL, Schaefer HG, Swedberg K, Wiltse CG, Karlsson MO. Pharmacodynamic models for the cardiovascular effects of moxonidine in patients with congestive heart failure. British Journal of Clinical Pharmacology 2001. https://doi.org/10.1046/j.1365-2125.2001.01320.x.
[5]
Francheteau P, Steimer J-L, Merdjan H, Guerret M, Dubray C. A mathematical model for dynamics of cardiovascular drug action: Application to intravenous dihydropyridines in healthy volunteers. Journal of Pharmacokinetics and Biopharmaceutics 1993. https://doi.org/10.1007/BF01059111.
[6]
Sumner DJ, Meredith PA, Howie CA, Elliott HL. Initial blood pressure as a predictor of the response to antihypertensive therapy. British Journal of Clinical Pharmacology 1988. https://doi.org/10.1111/j.1365-2125.1988.tb05310.x.
[7]
Patel HC, Hayward C, Ozdemir BA, Rosen SD, Krum H, Lyon AR, et al. Magnitude of blood pressure reduction in the placebo arms of modern hypertension trials: Implications for trials of renal denervation. Hypertension 2015;65:401–6. https://doi.org/10.1161/HYPERTENSIONAHA.114.04640.
[8]
Overgaard R, Ingwersen S, Tornøe C. Establishing Good Practices for Exposure-Response Analysis of Clinical Endpoints in Drug Development: Exposure-Response Analysis of Clinical Endpoints. CPT Pharmacometrics Syst Pharmacol 2015;4:565–75. https://doi.org/10.1002/psp4.12015.
[9]
Hammarlund-Udenaes M, Benet LZ. Furosemide pharmacokinetics and pharmacodynamics in health and disease: An update. Journal of Pharmacokinetics and Biopharmaceutics 1989;17:1–46. https://doi.org/10.1007/BF01059086.
[10]
Ribbing J, Jonsson EN. Power, selection bias and predictive performance of the population pharmacokinetic covariate model. Journal of Pharmacokinetics and Pharmacodynamics 2004;31:109–34. https://doi.org/10.1023/B:JOPA.0000034404.86036.72.