Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization. “Mycotoxins”. 2018. Available online: https://www.who.int/news-room/fact-sheets/detail/mycotoxins (accessed on 18 August 2022).
- IARC. “Monograph IARC Aflatoxins”. 2002. Available online: https://monographs.iarc.fr/wp-content/uploads/2018/06/mono100F-23.pdf (accessed on 25 May 2020).
- Smith, M.-C.; Madec, S.; Coton, E.; Hymery, N. Natural Co-Occurrence of Mycotoxins in Foods and Feeds and Their in vitro Combined Toxicological Effects. Toxins 2016, 8, 94. [Google Scholar] [CrossRef] [PubMed]
- Rotimi, O.A.; Rotimi, S.O.; Goodrich, J.M.; Adelani, I.B.; Agbonihale, E.; Talabi, G. Time-Course Effects of Acute Aflatoxin B1 Exposure on Hepatic Mitochondrial Lipids and Oxidative Stress in Rats. Front. Pharmacol. 2019, 10, 467. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Claeys, L.; Romano, C.; De Ruyck, K.; Wilson, H.; Fervers, B.; Korenjak, M.; Zavadil, J.; Gunter, M.J.; De Saeger, S.; De Boevre, M.; et al. Mycotoxin exposure and human cancer risk: A systematic review of epidemiological studies. Compr. Rev. Food Sci. Food Saf. 2020, 19, 1449–1464. [Google Scholar] [CrossRef]
- Lewis, C.; Smith, J.; Anderson, J.; Freshney, R. Increased cytotoxicity of food-borne mycotoxins toward human cell lines in vitro via enhanced cytochrome p450 expression using the MTT bioassay. Mycopathologia 1999, 148, 97–102. [Google Scholar] [CrossRef]
- He, X.-Y.; Tang, L.; Wang, S.-L.; Cai, Q.-S.; Wang, J.-S.; Hong, J.-Y. Efficient activation of aflatoxin B1 by cytochrome P450 2A13, an enzyme predominantly expressed in human respiratory tract. Int. J. Cancer 2006, 118, 2665–2671. [Google Scholar] [CrossRef] [PubMed]
- Yang, X.-J.; Lu, H.-Y.; Li, Z.-Y.; Bian, Q.; Qiu, L.-L.; Li, Z.; Liu, Q.; Li, J.; Wang, X.; Wang, S.-L. Cytochrome P450 2A13 mediates aflatoxin B1-induced cytotoxicity and apoptosis in human bronchial epithelial cells. Toxicology 2012, 300, 138–148. [Google Scholar] [CrossRef]
- Deng, J.; Zhao, L.; Zhang, N.-Y.; Karrow, N.A.; Krumm, C.S.; Qi, D.-S.; Sun, L.-H. Aflatoxin B1 metabolism: Regulation by phase I and II metabolizing enzymes and chemoprotective agents. Mutat. Res. Mol. Mech. Mutagen. 2018, 778, 79–89. [Google Scholar] [CrossRef]
- Gallagher, L.C.; Wienkers, P.L.; Stapleton, K.L.; Kunze, E.; Eaton, D.L. Role of Human Microsomal and Human Complementary DNA-Expressed Cytochromes P4501A2 and P4503A4 in the Bioactivation of Aflatoxin 1. 1994. Available online: https://cancerres.aacrjournals.org/content/canres/54/1/101.full.pdf (accessed on 28 May 2020).
- Kumar, V.; Bahuguna, A.; Ramalingam, S.; Dhakal, G.; Shim, J.-J.; Kim, M. Recent technological advances in mechanism, toxicity, and food perspectives of enzyme-mediated aflatoxin degradation. Crit. Rev. Food Sci. Nutr. 2021, 62, 5395–5412. [Google Scholar] [CrossRef]
- Kamdem, L.K.; Meineke, I.; Gödtel-Armbrust, U.; Brockmöller, J.; Wojnowski, L. Dominant Contribution of P450 3A4 to the Hepatic Carcinogenic Activation of Aflatoxin B1. Chem. Res. Toxicol. 2006, 19, 577–586. [Google Scholar] [CrossRef]
- DEfsa Panel on Contaminants in the Food Chain (Contam); Schrenk, D.; Bignami, M.; Bodin, L.; Chipman, J.K.; Del Mazo, J.; Grasl-Kraupp, B.; Hogstrand, C.; Hoogenboom, L.; Leblanc, J.; et al. Risk assessment of aflatoxins in food. EFSA J. 2020, 18, e06040. [Google Scholar] [CrossRef]
- Salhab, A.S.; Abramson, F.P.; Geelhoed, G.W.; Edwards, G.S. Aflatoxicol M1, A New Metabolite of Aflatoxicol. Xenobiotica 1977, 7, 401–408. [Google Scholar] [CrossRef] [PubMed]
- EFSA. Aflatoxins in food | EFSA. 2006. Available online: https://www.efsa.europa.eu/en/topics/topic/aflatoxins-food (accessed on 17 January 2023).
- Gomez, K.S.; Roldán, E.C.; Sosa, R.; Munguía-Pérez, R. Mycotoxins and Climate Change. In The Impact of Climate Change on Fungal Diseases; Springer: Berlin/Heidelberg, Germany, 2022; pp. 239–256. [Google Scholar] [CrossRef]
- Zain, M.E. Impact of mycotoxins on humans and animals. J. Saudi Chem. Soc. 2011, 15, 129–144. [Google Scholar] [CrossRef] [Green Version]
- FAO. Worldwide Regulations for Mycotoxins in Food and Feed in 2003. 2003. Available online: http://www.fao.org/docrep/007/y5499e/y5499e06.htm#bm06.1 (accessed on 12 April 2016).
- PUdomkun, P.; Wiredu, A.N.; Nagle, M.; Bandyopadhyay, R.; Müller, J.; Vanlauwe, B. Mycotoxins in Sub-Saharan Africa: Present situation, socio-economic impact, awareness, and outlook. Food Control 2017, 72, 110–122. [Google Scholar] [CrossRef]
- Azziz-Baumgartner, E.; Lindblade, K.; Gieseker, K.; Rogers, H.S.; Kieszak, S.; Njapau, H.; Schleicher, R.L.; McCoy, L.F.; Misore, A.; Decock, K.M.; et al. Case–Control Study of an Acute Aflatoxicosis Outbreak, Kenya, 2004. Environ. Health Perspect. 2005, 113, 1779–1783. [Google Scholar] [CrossRef] [PubMed]
- Darwish, W.S.; Ikenaka, Y.; Nakayama, S.M.; Ishizuka, M. An Overview on Mycotoxin Contamination of Foods in Africa. J. Vet. Med. Sci. 2014, 76, 789–797. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kamala, A.; Shirima, C.; Jani, B.; Bakari, M.; Sillo, H.; Rusibamayila, N.; De Saeger, S.; Kimanya, M.; Gong, Y.; Simba, A.; et al. Outbreak of an acute aflatoxicosis in Tanzania during 2016. World Mycotoxin J. 2018, 11, 311–320. [Google Scholar] [CrossRef]
- WHO. Health Emergency Information and Risk Assessment. 2019. Available online: https://apps.who.int/iris/bitstream/handle/10665/326465/OEW33-1218082019.pdf (accessed on 24 March 2020).
- Zeng, D.; Lin, Z.; Zeng, Z.; Fang, B.; Li, M.; Cheng, Y.-H.; Sun, Y. Assessing Global Human Exposure to T-2 Toxin via Poultry Meat Consumption Using a Lifetime Physiologically Based Pharmacokinetic Model. J. Agric. Food Chem. 2019, 67, 1563–1571. [Google Scholar] [CrossRef]
- Fæste, C.K.; Ivanova, L.; Sayyari, A.; Hansen, U.; Sivertsen, T.; Uhlig, S. Prediction of deoxynivalenol toxicokinetics in humans by in vitro-to-in vivo extrapolation and allometric scaling of in vivo animal data. Arch. Toxicol. 2018, 92, 2195–2216. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gilbert-Sandoval, I.; Wesseling, S.; Rietjens, I.M.C.M. Predicting the Acute Liver Toxicity of Aflatoxin B1 in Rats and Humans by an In Vitro–In Silico Testing Strategy. Mol. Nutr. Food Res. 2020, 64, e2000063. [Google Scholar] [CrossRef] [PubMed]
- Rodgers, T.; Leahy, D.; Rowland, M. Physiologically Based Pharmacokinetic Modeling 1: Predicting the Tissue Distribution of Moderate-to-Strong Bases. J. Pharm. Sci. 2005, 94, 1259–1276. [Google Scholar] [CrossRef]
- Jubert, C.; Mata, J.; Bench, G.; Dashwood, R.; Pereira, C.; Tracewell, W.; Turteltaub, K.; Williams, D.; Bailey, G. Effects of Chlorophyll and Chlorophyllin on Low-Dose Aflatoxin B1 Pharmacokinetics in Human Volunteers. Cancer Prev. Res. 2009, 2, 1015–1022. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- PubChem. Aflatoxin B1 | C17H12O6–PubChem. National Center for Biotechnology Information. PubChem Database. 2017. Available online: https://pubchem.ncbi.nlm.nih.gov/compound/Aflatoxin-B1#section=Other-Experimental-Properties (accessed on 10 August 2022).
- Varma, M.V.; Steyn, S.J.; Allerton, C.; El-Kattan, A.F. Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS). Pharm. Res. 2015, 32, 3785–3802. [Google Scholar] [CrossRef] [PubMed]
- Lootens, O.; De Boevre, M.; Gasthuys, E.; Van Bocxlaer, J.; Vermeulen, A.; De Saeger, S. Unravelling the pharmacokinetics of aflatoxin B1: In vitro determination of Michaelis–Menten constants, intrinsic clearance and the metabolic contribution of CYP1A2 and CYP3A4 in pooled human liver microsomes. Front. Microbiol. 2022, 13, 3258. [Google Scholar] [CrossRef] [PubMed]
- De Bruyn, T.; Ufuk, A.; Cantrill, C.; Kosa, R.E.; Bi, Y.-A.; Niosi, M.; Modi, S.; Rodrigues, A.D.; Tremaine, L.M.; Varma, M.V.S.; et al. Predicting Human Clearance of Organic Anion Transporting Polypeptide Substrates Using Cynomolgus Monkey: In Vitro–In Vivo Scaling of Hepatic Uptake Clearance. Drug Metab. Dispos. 2018, 46, 989–1000. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Loe, D.W.; Stewart, R.K.; Massey, T.E.; Deeley, R.G.; Cole, S.P.C. ATP-Dependent Transport of Aflatoxin B1 and Its Glutathione Conjugates by the Product of the Multidrug Resistance Protein (MRP) Gene. Mol. Pharmacol. 1997, 51, 1034–1041. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Vildhede, A.; Wiśniewski, J.R.; Norén, A.; Karlgren, M.; Artursson, P. Comparative Proteomic Analysis of Human Liver Tissue and Isolated Hepatocytes with a Focus on Proteins Determining Drug Exposure. J. Proteome Res. 2015, 14, 3305–3314. [Google Scholar] [CrossRef]
- Barter, Z.E.; Tucker, G.T.; Rowland-Yeo, K. Differences in Cytochrome P450-Mediated Pharmacokinetics Between Chinese and Caucasian Populations Predicted by Mechanistic Physiologically Based Pharmacokinetic Modelling. Clin. Pharmacokinet. 2013, 52, 1085–1100. [Google Scholar] [CrossRef]
- DuBois, D.; DuBois, E.F. A formula to estimate the approximate surface area if height and weight be known. Nutrition 1989, 5, 303–311; discussion 312–313. [Google Scholar]
- O Nwoye, L. Body surface area of Africans: A study based on direct measurements of Nigerian males. Hum. Biol. 1989, 61, 439–457. [Google Scholar]
- Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A New Equation to Estimate Glomerular Filtration Rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef]
- Rajman, I.; Knapp, L.; Morgan, T.; Masimirembwa, C. African Genetic Diversity: Implications for Cytochrome P450-mediated Drug Metabolism and Drug Development. Ebiomedicine 2017, 17, 67–74. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Peters, S.A.; Dolgos, H. Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them. Clin. Pharmacokinet. 2019, 58, 1355–1371. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Page, K.M. Validation of Early Human Dose Prediction: A Key Metric for Compound Progression in Drug Discovery. Mol. Pharm. 2016, 13, 609–620. [Google Scholar] [CrossRef] [PubMed]
- Persuad, N. EMLs Around the World. WHO Bulletin. 2019. Available online: https://global.essentialmeds.org/dashboard/medicines/215 (accessed on 15 August 2022).
- Food and Drug Administration. Compliance Policy Guide Sec. 570.375 Aflatoxins in Peanuts and Peanut Products: Guidance for FDA Staff. June 2021. Available online: https://www.fda.gov/media/72073/download (accessed on 10 August 2022).
- EUR-Lex–02006R1881-20100701–EN–EUR-Lex. 2006. Available online: https://eur-lex.europa.eu/eli/reg/2006/1881/2010-07-01 (accessed on 14 February 2023).
- EFSA Scientific Committee Statement on the applicability of the Margin of Exposure approach for the safety assessment of impurities which are both genotoxic and carcinogenic in substances added to food/feed. EFSA J. 2012, 10, 2578. [CrossRef]
Physicochemical Properties and Blood Binding | ||
---|---|---|
Parameter | Model Input Value | Reference |
Mw (g/moL) | 312.27 | [29] |
LogPo:w | 1.60 | [29] |
Compound type | neutral | [29] |
ECCS | Class 2 | [30] |
B:P | 1.03 | Predicted * |
fu,p | 0.17 | [26] |
Absorption | ||
fa | 0.99 | Predicted * |
ka | 2.39 | Predicted * |
Peff,man (10−4 cm/s) | 5.47 | predicted * |
Ptrans,0 (10−6 cm/s) | 135.8 | Predicted * |
Absorption and Metabolism (ADAM) Model | ||
Formulation—Diffusion Layer Model—Aqueous Phase Solubility—Solid State 1 | ||
S0 | 1.24 | Predicted * |
Distribution | ||
Full PBPK model | ||
Vss (L/kg) | 0.33 | prediction method 3 |
Tissue: plasma partition coefficients/Kp scalar = 1 | ||
Adipose | 0.44 | predicted * |
Bone | 0.15 | predicted * |
Brain | 0.55 | predicted * |
Gut | 0.36 | predicted * |
Pancreas | 0.26 | predicted * |
Heart | 0.37 | predicted * |
Kidney | 0.36 | predicted * |
Liver | 0.44 | predicted * |
Lung | 0.33 | predicted * |
Muscle | 0.23 | predicted * |
Skin | 0.28 | predicted * |
Spleen | 0.44 | predicted * |
Elimination | ||
Enzyme kinetics | ||
CYPs | Recombinant | |
CYP3A4 Km | 49.60 µM | Experimental [31] |
CYP3A4 Vmax | 88.10 pmol/min/pmol CYP | Experimental [31] |
CYP3A4 ISEF | 0.50 | Experimental [31] |
CYP1A2 Km | 58.20 µM | Experimental [31] |
CYP1A2 Vmax | 199.00 pmol/min/pmol CYP | Experimental [31] |
ISEF | 1.42 | Experimental [31] |
Interaction | ||
CYP1A2 Ki | 10.2 µM | Experimental [31] |
Transport | ||
Using permeability limited liver model | ||
CLPD (mL/min/million hepatocytes) | 0.05 | derived from [32] |
fu,IW | 0.35 | predicted * |
fu,EW | 0.17 | predicted * |
Drug concentration for passive permeability: unbound (ionized and unionized) Sinusoidal: Efflux: ABCC3 (MRP3) | ||
Jmax (pmol/min/million cells) | 180.00 | [33] |
Km (µM) | 0.19 | |
fu,inc | 1.00 | |
RAF/REF | 2.50 | [34] |
Height | |||
MALE | FEMALE | ||
C0 | 161.780 | C0 | 155.376 |
C1 | 0.359 | C1 | 0.207 |
C2 | −0.00429 | C2 | −0.00268 |
CV (%) | 7.33 | CV (%) | 5.83 |
Weight | |||
MALE | FEMALE | ||
C0 | 2.97 | C0 | 3.19 |
C1 | 0.007 | C1 | 0.007 |
CV (%) | 21.1 | CV (%) | 26.38 |
Black South African | Sim-NEur Caucasian | Sim-Chinese Healthy Volunteer | |||||||
---|---|---|---|---|---|---|---|---|---|
CYP450 Enzyme | Abundance (pmol/mg Protein)/CV | Phenotype Frequency | Abundance (pmol/mg Protein)/CV | Phenotype Frequency | Abundance (pmol/mg Protein)/CV | Phenotype Frequency | |||
EM | PM | EM | PM | EM | PM | ||||
CYP1A2 | 52/67% | 1 | 0 | 52/67% | 1 | 0 | 42/50% | 1 | 0 |
CYP2B6 | 6.9/122% | 0.85 | 0.15 | 21.6/68% | 0.40 | 0.10 | 6.7/63% | 0.52 | 0.07 |
CYP2C9 | 73/ 54% | 0.98 | 0.02 | 77.7/64% | 0.66 | 0.019 | 87.6/55% | 0.93 | 0.003 |
CYP2C19 | 14/106% | 0.96 | 0.04 | 4.4/52% | 0.42 | 0.023 | 4.4/52% | 0.40 | 0.13 |
CYP2D6 | 8/61% | 0.97 | 0.03 | 9.4/65% | 0.57 | 0.08 | 10.47/65% | 0.60 | 0.003 |
CYP3A4 | 137/41% | 1 | 0 | 137/41% | 1 | 0 | 120/33% | 1 | 0 |
CYP3A5 | 71/78% | 0.82 | 0.18 | 103/65% | 0.17 | 0.83 | 82.3/68% | 0.42 | 0.58 |
CYP3A7 | 35.4/61% | 0.12 | 0.88 | 35.4/61% | 0.12 | 0.88 | 14/71% | 0.12 | 0.88 |
parameter | parameter value | CV (%) | parameter value | CV (%) | parameter value | CV (%) | |||
LV (L) | 1.924 | 12 | 1.651 | 12 | 1.403 | 12 | |||
MPPGL (mg/g) | 39.79 | N.A. | 39.79 | N.A. | 39.45 | N.A. | |||
LD (g/L) | 1080 | N.A. | 1080 | N.A. | 1080 | N.A. | |||
Hematocrit (%) (male) | 43 | 6.51 | 43 | 6.5 | 45.3 | 9.5 | |||
Hematocrit (%) (female) | 38 | 7.13 | 38 | 7.1 | 40.5 | 10.9 | |||
AGP (g/L) (male) | 0.811 | 15 | 0.739 | 23 | 0.683 | 23 | |||
AGP (g/L) (female) | 0.791 | 13 | 0.715 | 24 | 0.575 | 24 | |||
HSA (g/L) (male) | 50.34 | 10 | 50.34 | 10 | 50.34 | 10 | |||
HSA (g/L) (female) | 49.38 | 10 | 49.38 | 10 | 49.38 | 10 | |||
Weibull α (male) | 1.47 | N.A. | 5.47 | N.A. | 1.5 | N.A. | |||
Weibull β (male) | 30.17 | N.A. | 66.5 | N.A. | 19 | N.A. | |||
Weibull α (female) | 1.47 | N.A. | 5.22 | N.A. | 4.48 | N.A. | |||
Weibull β (female) | 32.8 | N.A. | 68.57 | N.A. | 53.4 | N.A. |
Drug | Dose (mg) QD | CYP3A4/CYP1A2 Substrate/Inhibitor/Inducer | Drug Class |
---|---|---|---|
artemether | 20 | CYP3A4 substrate | antimalarial |
atazanavir | 200 | CYP3A4 substrate CYP3A4 inhibitor | protease inhibitor |
carbamazepine | 200 | CYP3A4 substrate CYP3A4 inducer | anticonvulsant |
ciprofloxacin | 250 | CYP1A2 inhibitor | quinolone antibiotics |
efavirenz | 600 | CYP3A4 inducer | non-nucleoside reverse transcriptase inhibitor |
ethinylestradiol | 0.035 | CYP3A4 substrate CYP1A2 inhibitor | estrogen |
phenobarbital | 100 | CYP3A4 and CYP1A2 inducer | barbiturate |
phenytoin | 100 | CYP3A4 and CYP1A2 inducer | anticonvulsant |
fluconazole | 50 | CYP3A4 inhibitor | triazole antifungal |
fluoxetine | 20 | CYP3A4 inhibitor | selective serotonin reuptake inhibitor |
midazolam | 5 | CYP3A4 substrate | benzodiazepine |
nifedipine | 20 | CYP3A4 substrate | calcium channel blocker |
rifampicin | 600 | CYP1A2 inducer CYP3A4 inducer | antimycobacterial |
ritonavir | 600 BID | CYP3A4 substrate CYP3A4 inhibitor | protease inhibitor |
simvastatin | 20 | CYP3A4 substrate | statins |
Observed Data (Mean ± SD) | Predicted Data (Mean ± SD) | Predicted/Observed Ratio | |
---|---|---|---|
Cmax (pg/mL) | 0.941 ± 0.154 | 1.02 ± 0.035 | 1.08 |
AUC0–24 h (pg/mL.h) | 12.4 ± 1.8 | 9.87 ± 0.825 | 0.80 |
Tmax (h) | 1.02 ± 0.31 h | 1.64 ± 0.075 h | 1.61 |
AFE on Cp | 1.12 | ||
AAFE on Cp | 1.35 |
Sim-Chinese Healthy Volunteers | North European Caucasian | Black South African | |
---|---|---|---|
mean Cmax (pg/mL) | 0.967 | 0.740 | 0.755 |
mean Tmax (h) | 1.92 | 1.67 | 1.64 |
mean AUC0–24 h (pg/mL.h) | 9.85 | 6.78 | 6.24 |
mean CL (L/h) | 4.62 | 6.52 | 8.78 |
AFB1 Alone | AFB1 + Drug | Ratio of PK Parameters (with Drug/without Drug) | |||
---|---|---|---|---|---|
ME | +Atazanavir (200 mg) QD | ME | |||
Cmax (pg/mL) | 1.19 | 0.076 | 1.69 | 0.12 | 1.39 |
Tmax (h) | 0.96 | 0.055 | 1.09 | 0.07 | 1.14 |
AUC0-inf (pg/mL.h) | 11.7 | 1.21 | 23.7 | 2.54 | 2.09 |
Cmin (pg/mL) | 0.18 | 0.0091 | 0.55 | 0.0055 | 3.10 |
CL (L/h) | 5.82 | 0.58 | 3.12 | 0.31 | 0.54 |
+carbamazepine (200 mg) QD | |||||
Cmax (pg/mL) | 1.21 | 0.08 | 1.00 | 0.063 | 0.83 |
Tmax (h) | 0.96 | 0.06 | 0.92 | 0.055 | 0.96 |
AUC0-inf (pg/mL.h) | 12.09 | 1.24 | 8.92 | 0.91 | 0.74 |
Cmin (pg/mL) | 0.19 | 0.00096 | 0.12 | 0.00048 | 0.62 |
CL (L/h) | 5.69 | 0.57 | 7.28 | 0.72 | 1.28 |
+ciprofloxacin (250 mg) QD | |||||
Cmax (pg/mL) | 1.20 | 0.075 | 1.52 | 0.093 | 1.27 |
Tmax (h) | 0.95 | 0.055 | 1.28 | 0.08 | 1.35 |
AUC0-inf (pg/mL.h) | 11.8 | 1.22 | 17.42 | 1.68 | 1.47 |
Cmin (pg/mL) | 0.19 | 0.00092 | 0.28 | 0.00188 | 1.51 |
CL (L/h) | 5.86 | 0.58 | 3.21 | 0.31 | 0.55 |
+efavirenz (600 mg) QD | |||||
Cmax (pg/mL) | 1.21 | 0.082 | 0.80 | 0.0053 | 0.66 |
Tmax (h) | 0.95 | 0.06 | 0.71 | 0.045 | 0.75 |
AUC0-inf (pg/mL.h) | 12.20 | 1.25 | 4.41 | 0.44 | 0.36 |
Cmin (pg/mL) | 0.20 | 0.0090 | 0.03 | 0.00006 | 0.16 |
CL (L/h) | 5.91 | 0.59 | 15.52 | 1.62 | 2.63 |
+phenobarbital (100 mg) QD | |||||
Cmax (pg/mL) | 1.21 | 0.078 | 0.78 | 0.055 | 0.64 |
Tmax (h) | 0.95 | 0.06 | 0.80 | 0.05 | 0.84 |
AUC0-inf (pg/mL.h) | 12.1 | 1.25 | 5.39 | 0.56 | 0.45 |
Cmin (pg/mL) | 0.20 | 0.009 | 0.05 | 0.00047 | 0.25 |
CL (L/h) | 5.91 | 0.59 | 13.60 | 1.38 | 2.30 |
+phenytoin (100 mg) QD | |||||
Cmax (pg/mL) | 1.21 | 0.08 | 0.95 | 0.063 | 0.79 |
Tmax (h) | 0.96 | 0.06 | 0.89 | 0.055 | 0.93 |
AUC0-inf (pg/mL.h) | 12.2 | 1.26 | 8.09 | 0.82 | 0.66 |
Cmin (pg/mL) | 0.20 | 0.0095 | 0.11 | 0.00284 | 0.55 |
CL (L/h) | 5.78 | 0.57 | 8.78 | 0.87 | 1.52 |
+fluconazole (50 mg) QD | |||||
Cmax (pg/mL) | 1.20 | 0.064 | 1.35 | 0.07 | 1.13 |
Tmax (h) | 0.95 | 0.06 | 1.00 | 0.06 | 1.05 |
AUC0-inf (pg/mL.h) | 12.0 | 0.8 | 15.0 | 0.95 | 1.25 |
Cmin (pg/mL) | 0.19 | 0.00113 | 0.267 | 0.00201 | 1.41 |
CL (L/h) | 5.88 | 0.92 | 4.75 | 0.79 | 0.81 |
+rifampicin (600 mg) QD | |||||
Cmax (pg/mL) | 1.19 | 0.08 | 0.64 | 0.051 | 0.54 |
Tmax (h) | 0.96 | 0.055 | 0.71 | 0.045 | 0.74 |
AUC0-inf (pg/mL.h) | 11.7 | 1.21 | 3.02 | 0.31 | 0.26 |
Cmin (pg/mL) | 0.18 | 0.0090 | 0.013 | 0.00001 | 0.07 |
CL (L/h) | 5.82 | 0.575 | 24.03 | 2.47 | 4.13 |
+ ritonavir (600 mg) BID | |||||
Cmax (pg/mL) | 1.21 | 0.078 | 1.95 | 0.15 | 1.56 |
Tmax (h) | 0.95 | 0.055 | 1.10 | 0.07 | 1.16 |
AUC0-inf (pg/mL.h) | 12.2 | 1.21 | 29.0 | 3.1 | 2.50 |
Cmin (pg/mL) | 0.20 | 0.0090 | 0.75 | 0.078 | 3.75 |
CL (L/h) | 5.91 | 0.575 | 2.78 | 0.27 | 0.47 |
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Lootens, O.; De Boevre, M.; Ning, J.; Gasthuys, E.; Van Bocxlaer, J.; De Saeger, S.; Vermeulen, A. Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs. Pharmaceutics 2023, 15, 894. https://doi.org/10.3390/pharmaceutics15030894
Lootens O, De Boevre M, Ning J, Gasthuys E, Van Bocxlaer J, De Saeger S, Vermeulen A. Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs. Pharmaceutics. 2023; 15(3):894. https://doi.org/10.3390/pharmaceutics15030894
Chicago/Turabian StyleLootens, Orphélie, Marthe De Boevre, Jia Ning, Elke Gasthuys, Jan Van Bocxlaer, Sarah De Saeger, and An Vermeulen. 2023. "Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs" Pharmaceutics 15, no. 3: 894. https://doi.org/10.3390/pharmaceutics15030894
APA StyleLootens, O., De Boevre, M., Ning, J., Gasthuys, E., Van Bocxlaer, J., De Saeger, S., & Vermeulen, A. (2023). Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs. Pharmaceutics, 15(3), 894. https://doi.org/10.3390/pharmaceutics15030894