Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis

Suri, Ajit; Chapel, Sunny; Lu, Chuang; Venkatakrishnan, Karthik

doi:10.1002/cpt.155

Cited by 23 publications

(11 citation statements)

References 18 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, PK profiles of drugs are expected to differ in cancer populations compared with profiles in healthy subjects. In many cases, clearance of anti-cancer drugs decreases in cancer patients compared with healthy individuals (Piotrovsky et al, 1998;Houk et al, 2009;Hudachek et al, 2010) for various reasons, including co-morbidities, such as hepatic and renal impairment in cancer patients (Suri et al, 2015). Another possible reason may be changes in MPPGL or CPPGL and differences in the expression of enzymes and transporters (Gao et al, 2016;Billington et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Hepatic Scaling Factors for In Vitro–In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis

et al. 2021

View full text Add to dashboard Cite

In vitro-in vivo extrapolation (IVIVE) linked with physiologically based pharmacokinetic (PBPK) modelling is used to predict the fates of drugs in patients. Ideally, the IVIVE-PBPK models should incorporate "systems" information accounting for characteristics of the specific target population. There is a paucity of such scaling factors in cancer, particularly microsomal protein per gram of liver (MPPGL) and cytosolic protein per gram of liver (CPPGL). In this study, cancerous and histologically normal liver tissue from 16 patients with colorectal liver metastasis (CRLM) were fractionated to microsomes and cytosol.Protein content was measured in homogenates, microsomes and cytosol. The loss of microsomal protein during fractionation was accounted for using corrections based on NADPH cytochrome P450 reductase activity in different matrices. MPPGL was significantly lower in cancerous tissue (24.8 ± 9.8 mg/g) than histologically normal tissue (39.0 ± 13.8 mg/g). CPPGL in cancerous tissue was 42.1 ± 12.9 mg/g compared with 56.2 ± 16.9 mg/g in normal tissue. No correlations between demographics (sex, age and BMI) and MPPGL or CPPGL were apparent in the data. The generated scaling factors together with assumptions regarding the relative volumes of cancerous versus non-cancerous tissue were used to simulate plasma exposure of drugs with different extraction ratios. The PBPK simulations revealed a substantial difference in drug exposure (AUC), up to 3.3-fold, when using typical scaling factors (healthy population) instead of disease-related parameters in cancer population. These indicate the importance of using population-specific scalars in IVIVE-PBPK for different disease states.

show abstract

Section: Discussionmentioning

confidence: 99%

Hepatic Scaling Factors for In Vitro–In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The mechanistic nature of PBPK models permits integration of data from in vitro and in vivo studies and can interpolate between organisms (eg, animals to humans) as well as developmental stages (eg, adults to pediatrics). Such PBPK models can complement population PK modeling and simulation and facilitate implementation of a predict‐learn‐confirm approach in drug development . Johnson et al evaluated prediction of CL for 11 drugs, including gentamicin and vancomycin, in neonates, infants, and children with normal kidney and hepatic function and concluded that renal CL of gentamicin was underpredicted in children <5 years of age, whereas the prediction for vancomycin was reasonable .…”

Section: Discussionmentioning

confidence: 99%

Pharmacometric Approaches to Personalize Use of Primarily Renally Eliminated Antibiotics in Preterm and Term Neonates

Wilbaux

Fuchs

Samardžić

et al. 2016

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Sepsis remains a major cause of mortality and morbidity in neonates, and, as a consequence, antibiotics are the most frequently prescribed drugs in this vulnerable patient population. Growth and dynamic maturation processes during the first weeks of life result in large inter- and intrasubject variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics. In this review we (1) summarize the available population PK data and models for primarily renally eliminated antibiotics, (2) discuss quantitative approaches to account for effects of growth and maturation processes on drug exposure and response, (3) evaluate current dose recommendations, and (4) identify opportunities to further optimize and personalize dosing strategies of these antibiotics in preterm and term neonates. Although population PK models have been developed for several of these drugs, exposure-response relationships of primarily renally eliminated antibiotics in these fragile infants are not well understood, monitoring strategies remain inconsistent, and consensus on optimal, personalized dosing of these drugs in these patients is absent. Tailored PK/PD studies and models are useful to better understand relationships between drug exposures and microbiological or clinical outcomes. Pharmacometric modeling and simulation approaches facilitate quantitative evaluation and optimization of treatment strategies. National and international collaborations and platforms are essential to standardize and harmonize not only studies and models but also monitoring and dosing strategies. Simple bedside decision tools assist clinical pharmacologists and neonatologists in their efforts to fine-tune and personalize the use of primarily renally eliminated antibiotics in term and preterm neonates.

show abstract

“…This case illustrates how PBPK modeling can inform appropriate dosing of renal impairment (RI) patients in phase I/III studies and thereby enable characterization of safety and efficacy in the RI patients during the late stage of drug development 17 . Data from human ADME study revealed that orteronel (see last example) is a drug that is primarily cleared by kidney excretion.…”

Section: Dose Guidance For Renal Impairment Patients a Case Studymentioning

confidence: 99%

PBPK modeling and simulation in drug research and development

Zhuang¹,

2016

Acta Pharmaceutica Sinica B

292

215

View full text Add to dashboard Cite

Physiologically based pharmacokinetic (PBPK) modeling and simulation can be used to predict the pharmacokinetic behavior of drugs in humans using preclinical data. It can also explore the effects of various physiologic parameters such as age, ethnicity, or disease status on human pharmacokinetics, as well as guide dose and dose regiment selection and aid drug–drug interaction risk assessment. PBPK modeling has developed rapidly in the last decade within both the field of academia and the pharmaceutical industry, and has become an integral tool in drug discovery and development. In this mini-review, the concept and methodology of PBPK modeling are briefly introduced. Several case studies were discussed on how PBPK modeling and simulation can be utilized through various stages of drug discovery and development. These case studies are from our own work and the literature for better understanding of the absorption, distribution, metabolism and excretion (ADME) of a drug candidate, and the applications to increase efficiency, reduce the need for animal studies, and perhaps to replace clinical trials. The regulatory acceptance and industrial practices around PBPK modeling and simulation is also discussed.

show abstract

Physiologically based and population PK modeling in optimizing drug development: A predict–learn–confirm analysis

Cited by 23 publications

References 18 publications

Hepatic Scaling Factors for In Vitro–In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis

Hepatic Scaling Factors for In Vitro–In Vivo Extrapolation of Metabolic Drug Clearance in Patients with Colorectal Cancer with Liver Metastasis

Pharmacometric Approaches to Personalize Use of Primarily Renally Eliminated Antibiotics in Preterm and Term Neonates

PBPK modeling and simulation in drug research and development

Contact Info

Product

Resources

About