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Physiologically Based Pharmacokinetic Modeling of Propranolol in Relation to CYP2D6 Genotypes
Author(s) -
Lee Choong-Min,
Jung Eui-Hyun,
Cho Chang-Keun,
Oh Kyung-Yul,
Shin Hyo-Bin,
Lim Chang-Woo,
Byeon Ji-Young,
Lee Seok-Yong
Publication year - 2020
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2020.34.s1.03840
Subject(s) - physiologically based pharmacokinetic modelling , cyp2d6 , propranolol , pharmacokinetics , pharmacology , cyp1a2 , chemistry , adme , cytochrome p450 , chromatography , biology , medicine , biochemistry , metabolism
Propranolol, used to treat high blood pressure and irregular heart rate, is mainly metabolized by cytochrome P450 2D6 (CYP2D6) to 4‐hydroxypropranolol (4‐OHP) and CYP1A2 to N‐desisoprophylpropranolol (N‐DIP). Physiologically based pharmacokinetic (PBPK) modeling is a mathematical modeling technique for predicting the absorption, distribution, metabolism and excretion (ADME) of synthetic or natural chemical substances in humans and other animal species. We investigated whether the pharmacokinetics of propranolol was altered by the different CYP2D6 genotypes in Korean subjects. Then, through in vitro study of propranolol, the metabolic capacity of propranolol was measured according to the CYP2D6 genotypes. And we developed the PBPK model of propranolol related to CYP2D6 genetic polymorphism. Twenty‐five volunteers were grouped as CYP2D6 *wt ⁄ *wt ( *wt =1 or 2, n=7), CYP2D6 *1/ *10 (n=3), CYP2D6 *1 ⁄ *5 (n=4) and CYP2D6 *10 ⁄ *10 (n=11) according to their genotypes. Propranolol hydrochloride 40 mg (Pranol®) was administered orally once to each subject in these four groups. The CYP2D6 *1 , CYP2D6 *10 and CYP1A2 *1 were incubated with 0.5–100 μM propranolol for 60 min at 37 □. Then the metabolites were extracted, and the concentration was analyzed by liquid chromatography‐mass spectrometry. PBPK modeling of propranolol was developed and optimized using PK‐sim® software. And, validation of PBPK model was conducted by comparing the predicted values with observed values from comparison the pharmacokinetic studies. In clinical data, AUC end , AUC inf , C max and oral clearance of propranolol were significantly different in CYP2D6 genetic polymorphism (P<0.005, respectively). Depending on the physico‐chemical parameters and ADME of each genotype, PBPK model of propranolol was developed. For metabolism, input values of in vitro V max of 4‐OHP in the presence of recombinant enzyme for CYP2D6 *1, CYP2D6 *10 , and CYP1A2 *1 were 2.41, 0.344 and 0.383 pmol/min/pmol, respectively. And, input values of K m of 4‐OHP for CYP2D6 *1, CYP2D6 *10 , and CYP1A2 *1 were 2.852, 8.914 and 6.95 μM, respectively. Input values of in vitro V max of N‐DIP in the presence of recombinant enzyme for CYP2D6 *1, CYP2D6 *10 , and CYP1A2 *1 were 9.782, 7.891 and 11.18 pmol/min/pmol, respectively. And, input values of K m of N‐DIP for CYP2D6 *1, CYP2D6 *10 , and CYP1A2 *1 were 162.8, 339.1 and 158.4 μM, respectively. The AUC inf mean of simulated PBPK model were 111.77, 184.74, 296.13, 370.85 ng·h/ml in CYP2D6 *wt ⁄ *wt , CYP2D6 *1 / *10 , CYP2D6 *1 ⁄ *5 and CYP2D6 *10 ⁄ *10 , respectively. The developed PBPK model of propranolol successfully described the pharmacokinetics of each CYP2D6 genotype group and its simulated values were within acceptance criterion (99.998% confidence interval). These results will be beneficial in prescribing the appropriate dosage of propranolol considering inter‐individual differences through this mechanical approach. In addition, the PBPK modeling of propranolol in relation to CYP2D6 genotypes will be applicable to the treatment of various ethnic groups, ages, and patients.The observed and predicted pharmacokinetics for propranolol with acceptance criterion range in CYP2D6 genotype groups.GenotypeC max (ng/mL) AUC end (ng*h/mL) AUC inf (ng*h/mL) T max (h) T 1/2 (h)CYP2D6*wt/*wt (n=7)Observed 21.93±5.74 132.2±41.39 150.07±40.34 1.79±0.27 4.04±0.94Predicted30.70111.53111.771.902.83Aceeptance range 14.49–33.19 80.79–216.30 98.09–229.60 1.41–2.27 2.80–5.84CYP2D6*1/*10 (n=3)Observed 32.30±5.92 149.28±62.51 165.18±71.82 1.50±0.50 3.27±0.61Predicted34.74181.01184.741.954.37Aceeptance range 20.66–50.51 55.55–401.16 59.36–459.66 0.68–3.33 2.08–5.14CYP2D6*1/*5 (n=4)Observed 43.75±16.07 245.37±100.75 255.58±99.22 1.75±0.29 3.72±1.14Predicted51.88286.96296.131.955.02Aceeptance range 20.51–93.32 105.85–568.78 115.06–567.70 1.23–2.48 1.96–7.06CYP2D6*10/*10 (n=11)Observed 48.77±17.07 339.82±106.89 360.32±110.67 2.27±0.82 4.68±1.38Predicted62.70357.72370.852.005.14Aceeptance range 31.51–75.48 229.04–504.19 245.02–529.88 1.45–3.56 3.23–6.78