Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach.

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

Assessing the degree of hepatic ischemia-reperfusion injury using PBPK modeling of sodium fluorescein disposition in ex vivo machine-perfused livers.

Ischemia-reperfusion injury (IRI) is an intrinsic risk associated with liver transplantation. Ex vivo hepatic machine perfusion (MP) is an emerging organ preservation technique that can mitigate IRI, especially in livers subjected to prolonged warm ischemia time (WIT). However, a method to quantify the biological response to WIT during MP has not been established. Previous studies used physiologically-based pharmacokinetic (PBPK) modeling to demonstrate that a decrease in hepatic transport and biliary excretion of the tracer molecule sodium fluorescein (SF) could correlate with increasing WIT in situ. Furthermore, these studies proposed intracellular sequestration of the hepatocyte canalicular membrane transporter multi-drug resistance-associated protein 2 (MRP2) leading to decreased MRP2 activity (maximal transport velocity; Vmax) as the potential mechanism for decreased biliary SF excretion. We adapted an extant PBPK model to account for ex vivo hepatic MP and fit a 6-parameter version of this model to control time course measurements of SF in MP perfusate and bile. We then identified parameters whose values were likely insensitive to changes in WIT and fixed them to generate a reduced model with only 3 unknown parameters. Finally, we fit the reduced model to each individual biological replicate SF time course with differing WIT and found the mean estimated value for each parameter and compared them using a one-way ANOVA. We demonstrated that there was a significant decrease in the estimated value of Vmax for MRP2 at 30 min WIT. These studies provide the foundation for future studies investigating real-time assessment of liver viability during ex vivo MP.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic.

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Quantitative Proteomics for Translational Pharmacology and Precision Medicine: State of The Art and Future Outlook.

Over the past 20 years, quantitative proteomics has contributed a wealth of protein expression data, which are currently used for a variety of systems pharmacology applications, as a complement or a surrogate for activity of the corresponding proteins. A symposium at the 25th North American ISSX meeting, in Boston, in September 2023, was held to explore current and emerging applications of quantitative proteomics in translational pharmacology and strategies for improved integration into model-informed drug development based on practical experience of each of the presenters. A summary of the talks and discussions is presented in this perspective alongside future outlooks that were outlined for future meetings. Significance Statement This perspective explores current and emerging applications of quantitative proteomics in translational pharmacology and precision medicine, and outlines outlooks for improved integration into model-informed drug development.

The ADME characteristics of siRNA therapeutics and the opportunity to predict disposition in pregnant women.

Small interfering RNA (siRNA) therapeutics represent an emerging class of pharmacotherapy with the potential to address previously hard-to-treat diseases. Currently approved siRNA therapeutics include LNP-encapsulated siRNA and triGalNAc-conjugated siRNA. These siRNA therapeutics exhibit distinct pharmacokinetic characteristics and unique absorption, distribution, metabolism, and elimination (ADME) properties. As a new drug modality, limited clinical data are available for siRNA therapeutics in specific populations, including pediatrics, geriatrics, individuals with renal or hepatic impairment, and pregnant women, making dosing challenging. In this review, a mechanistic overview of the ADME properties of the five currently approved siRNA therapeutics is presented. A concise overview of the clinical data available for therapeutic siRNAs in special populations, focusing on the potential impact of physiological changes during pregnancy on siRNA disposition is provided. The utility of physiologically based pharmacokinetic (PBPK) modeling as a tool to elucidate the characteristics and disposition of siRNA therapeutics in pregnant women is explored. Additionally, opportunities to integrate known physiological alterations induced by pregnancy into PBPK models that incorporate siRNA ADME mechanisms to predict the effects of pregnancy on siRNA disposition are discussed. Clinical data regarding the use of therapeutic siRNA in special populations remains limited. Data for precise parameterization of maternal-fetal siRNA PBPK models is lacking presently and underscores the need for further research in this area. Addressing this gap in knowledge will not only enhance our understanding of siRNA pharmacokinetics during pregnancy but also advance possible development of siRNA therapeutics to treat pregnancy related conditions. Significance Statement This review proposes a framework on how siRNA disposition can be predicted in pregnancy based on mechanistic ADME information using physiologically based pharmacokinetic (PBPK) modeling. The mechanistic ADME information and available clinical data in special populations of currently FDA approved siRNA therapeutics are summarized. A detailed discussion on how physiological changes during pregnancy may affect siRNA disposition in pregnant women and on the opportunities to project siRNA disposition in pregnant women using PBPK modeling is provided.

Utility of PBPK Modeling in Predicting and Characterizing Clinical Drug Interactions.

Physiologically-based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions. With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late stage drug development and is often utilized to support regulatory packages for new drug applications. This minireview will briefly describe the approaches to predicting DDI utilizing PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. Significance Statement PBPK models have been a key tool in the characterization of various pharmacokinetic phenomenon, including drug-drug interactions. This minireview will highlight recent advancements and publications around PBPK DDI modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Physiologically Based Pharmacokinetic (PBPK) Modeling of small molecules: How Much Progress Have We Made?

Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and academic research. The use of PBPK models is continuously expanding with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiological changes into the models. Due to the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series gut compartments allowing modeling of physiological blood flow distribution within an organ and zonation of metabolic enzymes, and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This review suggests that awareness of the need for predefined criteria for model acceptance has increased but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance. Significance Statement PBPK modeling has become a mainstream application in academic literature and is broadly used for predictions, analysis and evaluation of pharmacokinetic data. Many significant advances have been made in developing advanced PBPK models that better capture human physiology but oftentimes sufficient justification for the chosen model acceptance criterion and model structure is still missing. This review provides a summary of the current landscape of PBPK applications used and highlights the needs for advancing PBPK modeling science and training in academia.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023).

PURPOSE: The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis. METHODS: We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses. RESULTS: A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field. CONCLUSION: We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia.

Drug dose response curves are ubiquitous in cancer biology, but these curves are often used to measure differential response in first-order effects: the effectiveness of increasing the cumulative dose delivered. In contrast, second-order effects (the variance of drug dose) are often ignored. Knowledge of second-order effects may improve the design of chemotherapy scheduling protocols, leading to improvements in tumor response without changing the total dose delivered. By considering treatment schedules with identical cumulative dose delivered, we characterize differential treatment outcomes resulting from high variance schedules (e.g. high dose, low dose) and low variance schedules (constant dose). We extend a previous framework used to quantify second-order effects, known as antifragility theory, to investigate the role of drug pharmacokinetics. Using a simple one-compartment model, we find that high variance schedules are effective for a wide range of cumulative dose values. Next, using a mouse-parameterized two-compartment model of 5-fluorouracil, we show that schedule viability depends on initial tumor volume. Finally, we illustrate the trade-off between tumor response and lean mass preservation. Mathematical modeling indicates that high variance dose schedules provide a potential path forward in mitigating the risk of chemotherapy-associated cachexia by preserving lean mass without sacrificing tumor response.

Modeled Hepatic/Plasma Exposures of Omeprazole Prescribed Alone in Cytochrome P450 2C19 Poor Metabolizers Are Likely Associated with Hepatic Toxicity Reported in a Japanese Adverse Event Database.

Omeprazole, a gastric acid pump inhibitor, is repeatedly administered and is oxidatively metabolized mainly by polymorphic cytochrome P450 2C19. The prescribed dosage of omeprazole was discontinued or reduced in 47 of the 135 patients who received omeprazole alone in this survey, as recorded in the Japanese Adverse Drug Event Report database. The days to onset of omeprazole-related disorders were 3-4 d (median) and 16 d for intravenous 20-40 mg and oral 20 mg daily doses, respectively, in 34 patients for whom relevant data were available. The maximum plasma concentration of omeprazole was pharmacokinetically modeled after a single oral 40-mg dose in P450 2C19-defective poor metabolizers and was 2.4-fold higher than that in extensive metabolizers. The modeled area under the hepatic concentration curves of omeprazole in P450 2C19 poor metabolizers after virtual daily 40-mg doses for 7 d was 5.2-fold higher than that in the extensive metabolizers. Omeprazole-induced P450 2C19 (approx. 2-fold), resulting in increased hepatic intrinsic clearance in repeated doses, was considered after the second day. Virtual plasma/hepatic exposure estimated using pharmacokinetic modeling in subjects with P450 2C19 poor metabolizers indicated that these exposure levels virtually estimated could be one of causal factors for unexpected hepatic disorders induced by prescribed omeprazole, such as those resulting from drug interactions with repeatedly co-administered medicines.

Modeled Hepatic/Plasma Exposures of Fluvastatin Prescribed Alone in Subjects with Impaired Cytochrome P450 2C9*3 as One of Possible Determinant Factors Likely Associated with Hepatic Toxicity Reported in a Japanese Adverse Event Database.

Fluvastatin is a 3-hydroxy-3-methylglutaryl CoA reductase inhibitor that competitively inhibits human cytochrome P450 (P450) 2C9 in vitro. Drug interactions between a variety of P450 2C9 substrates/inhibitors and fluvastatin can increase the incidence of fluvastatin-related hepatic or skeletal muscle toxicity in vivo. In this survey, the prescribed dosage of fluvastatin was reduced or discontinued in 133 of 164 patients receiving fluvastatin alone, as recorded in the Japanese Adverse Drug Event Report database of spontaneously reported events. The median days to onset of fluvastatin-related disorders were in the range 30-35 d in the 87 patients. Therefore, we aimed to focus on fluvastatin and, using the pharmacokinetic modeling technique, estimated the virtual plasma and hepatic exposures in subjects harboring the impaired CYP2C9*3 allele. The plasma concentrations of fluvastatin modeled after a virtual oral 20-mg dose increased in homozygotes with CYP2C9*3; the area under the plasma concentration curve was 4.9-fold higher than that in Japanese homozygotes for wild-type CYP2C9*1. The modeled hepatic concentrations of fluvastatin in patients with CYP2C9*3/*3 after virtual daily 20-mg doses for 7 d were 31-fold higher than those in subjects with CYP2C9*1/*1. However, heterozygous Chinese patients with CYP2C9*1/*3 reportedly have a limited elevation (1.2-fold) in plasma maximum concentrations. Virtual hepatic/plasma exposures in subjects harboring the impaired CYP2C9*3 allele estimated using pharmacokinetic modeling indicate that such exposure could be a causal factor for hepatic disorders induced by fluvastatin prescribed alone in a manner similar to that for interactions with a variety of co-administered drugs.

Meropenem pharmacokinetic/pharmacodynamic target attainment and clinical response in ICU patients: A prospective observational study.

BACKGROUND: Several studies report lack of meropenem pharmacokinetic/pharmacodynamic (PK/PD) target attainment (TA) and risk of therapeutic failure with intermittent bolus infusions in intensive care unit (ICU) patients. The aim of this study was to describe meropenem TA in an ICU population and the clinical response in the first 72 h after therapy initiation. METHODS: A prospective observational study of ICU patients ≥18 years was conducted from 2014 to 2017. Patients with normal renal clearance (NRC) and augmented renal clearance (ARC) and patients on continuous renal replacement therapy (CRRT) were included. Meropenem was administered as intermittent bolus infusions, mainly at a dose of 1 g q6h. Peak, mid, and trough levels were sampled at 24, 48, and 72 h after therapy initiation. TA was defined as 100% T > 4× MIC or trough concentration above 4× MIC. Meropenem PK was estimated using traditional calculation methods and population pharmacokinetic modeling (P-metrics®). Clinical response was evaluated by change in C-reactive protein (CRP), Sequential Organ Failure Assessment (SOFA) score, leukocyte count, and defervescence. RESULTS: Eighty-seven patients were included, with a median Simplified Acute Physiology (SAPS) II score 37 and 90 days mortality rate of 32%. Median TA was 100% for all groups except for the ARC group with 45.5%. Median CRP fell from 175 (interquartile range [IQR], 88-257) to 70 (IQR, 30-114) (p < .001) in the total population. A reduction in SOFA score was observed only in the non-CRRT groups (p < .001). CONCLUSION: Intermittent meropenem bolus infusion q6h gives satisfactory TA in an ICU population with variable renal function and CRRT modality, except for ARC patients. No consistent relationship between TA and clinical endpoints were observed.

Pharmacokinetic modeling reveals parameters that govern tumor targeting and delivery by a pH-Low Insertion Peptide (pHLIP).

A pH-Low Insertion Peptide (pHLIP) is a pH-sensitive peptide that undergoes membrane insertion, resulting in transmembrane helix formation, on exposure to acidity at a tumor cell surface. As a result, pHLIPs preferentially accumulate within tumors and can be used for tumor-targeted imaging and drug delivery. Here we explore the determinants of pHLIP insertion, targeting, and delivery through a computational modeling approach. We generate a simple mathematical model to describe the transmembrane insertion process and then integrate it into a pharmacokinetic model, which predicts the tumor vs. normal tissue biodistribution of the most studied pHLIP, "wild-type pHLIP," over time after a single intravenous injection. From these models, we gain insight into the various mechanisms behind pHLIP tumor targeting and delivery, as well as the various biological parameters that influence it. Furthermore, we analyze how changing the properties of pHLIP can influence the efficacy of tumor targeting and delivery, and we predict the properties for optimal pHLIP phenotypes that have superior tumor targeting and delivery capabilities compared with wild-type pHLIP.

Probabilistic risk assessment of residential exposure to electric arc furnace steel slag using Bayesian model of relative bioavailability and PBPK modeling of manganese.

Electric arc furnace (EAF) slag is a coproduct of steel production used primarily for construction purposes. Some applications of EAF slag result in residential exposures by incidental ingestion and inhalation of airborne dust. To evaluate potential health risks, an EAF slag characterization program was conducted to measure concentrations of metals and leaching potential (including oral bioaccessibility) in 38 EAF slag samples. Arsenic, hexavalent chromium, iron, vanadium, and manganese (Mn) were identified as constituents of interest (COIs). Using a probabilistic risk assessment (PRA) approach, estimated distributions of dose for COIs were assessed, and increased cancer risks and noncancer hazard quotients (HQs) at the 50th and 95th percentiles were calculated. For the residents near slag-covered roads, cancer risk and noncancer HQs were <1E - 6 and 1, respectively. For residential driveway or landscape exposure, at the 95th percentile, cancer risks were 1E - 6 and 7E - 07 based on oral exposure to arsenic and hexavalent chromium, respectively. HQs ranged from 0.07 to 2 with the upper bound due to ingestion of Mn among children. To expand the analysis, a previously published physiologically based pharmacokinetic (PBPK) model was used to estimate Mn levels in the globus pallidus for both exposure scenarios and further evaluate the potential for Mn neurotoxicity. The PBPK model estimated slightly increased Mn in the globus pallidus at the 95th percentile of exposure, but concentrations did not exceed no-observed-adverse-effect levels for neurological effects. Overall, the assessment found that the application of EAF slag in residential areas is unlikely to pose a health hazard or increased cancer risk.

Estimation of volatile organic compound exposure concentrations and time to reach a specific dermal absorption using physiologically based pharmacokinetic modeling.

A procedure was proposed to estimate dermal exposures based on a physiologically based pharmacokinetic (PBPK) model developed in rats. The study examined vapor concentrations ranging from 500 to 10,000 ppm for dibromomethane and 2,500 to 40,000 ppm for bromochloromethane. These concentrations were reconstructed based on chemical blood levels measured in 4 hr, with errors varying from 0.0% to 52.0%. The PBPK approach adequately predicted the blood concentrations and helped simulate contaminant transport through the stratum corneum and distribution in the body compartments. The proposed technique made it possible to estimate the skin absorption time (SAT) obtained from acute inhalation toxicity data. An inverse relationship exists between the SAT and exposure concentration. The method can be helpful in toxicology and risk assessment of hazardous volatile organic compounds.

Optimizing anidulafungin exposure across a wide adult body size range.

Echinocandins like anidulafungin are first-line therapies for candidemia and invasive candidiasis, but their dosing may be suboptimal in obese patients. Our objective was to quantify anidulafungin exposure in a cohort of adults across a wide body size range to test if body size affects anidulafungin pharmacokinetics (PK). We enrolled 20 adults between the ages of 18 and 80 years, with an equal distribution of patients above and below a body mass index of 30 kg/m2. A single 100-mg dose of anidulafungin was administered, followed by intensive sampling over 72 h. Population PK analysis was used to identify and compare covariates of anidulafungin PK parameters. Monte Carlo simulations were performed to compute the probability of target attainment (PTA) based on alternative dosing regimens. Participants (45% males) had a median (range) age of 45 (21-78) years and a median (range) weight of 82.7 (42.4-208.3) kg. The observed median (range) of AUC0-∞ was 106.4 (51.9, 138.4) mg∙h/L. Lean body weight (LBW) and adjusted body weight (AdjBW) were more influential than weight as covariates of anidulafungin PK parameters. The conventional 100 mg daily maintenance is predicted to have a PTA below 90% in adults with an LBW > 55 kg or an AdjBW > 75 kg. A daily maintenance dose of 150-200 mg is predicted in these heavier adults. Anidulafungin AUC0-∞ declines with increasing body size. A higher maintenance dose will increase the PTA compared to the current approach in obese patients.

Effects of Body Mass and Age on the Pharmacokinetics of Subcutaneous or Hyaluronidase-facilitated Subcutaneous Immunoglobulin G in Primary Immunodeficiency Diseases.

PURPOSE: To assess the pharmacokinetics (PK) of subcutaneous immunoglobulin (SCIG) and hyaluronidase-facilitated SCIG (fSCIG) therapy across body mass index (BMI) and age categories in patients with primary immunodeficiency diseases (PIDD) previously treated with intravenous immunoglobulin (IVIG). METHODS: Using our previously published integrated population PK model based on data from eight clinical trials, simulations were conducted to examine the effects of BMI and age on serum immunoglobulin G (IgG) PK after administration of SCIG 0.15 g/kg weekly or fSCIG 0.6 g/kg every 4 weeks in patients switching from stable IVIG. Patients were assumed to have baseline IgG trough concentrations of 7 g/L (hypothetical protective threshold). RESULTS: Mean steady-state serum IgG trough values (Cmin,ss or trough) increased with BMI and age. Mean Cmin,ss was 18% (SCIG) and 16% (fSCIG) higher in the obese than the healthy BMI group. Pediatric patients aged < 18 years had 8-22% (SCIG) and 4-20% (fSCIG) lower mean Cmin,ss values than adults, with the youngest group (2- < 6 years) having the lowest Cmin,ss. All patients across populations maintained Cmin,ss IgG concentrations of ≥ 7 g/L after switching to SCIG or fSCIG. CONCLUSION: Both SCIG and fSCIG successfully maintained trough values at or above the hypothetical protective threshold after switching from stable IVIG, irrespective of BMI or age. Differences in trough values between BMI groups and age groups (≤ 22%) may not warrant SCIG or fSCIG dose adjustments based on BMI or age alone; instead, the dosing paradigm should be guided by prior IVIG dose, individual IgG monitoring, and clinical findings.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast.

PURPOSE: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. THEORY: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points for accurate estimation of perfusion parameters. METHODS: The DCE-MRI data acquired in women with invasive breast cancer (N = 27) were used to retrospectively compare parameter maps fit to full and subsampled time courses. Simplicial complexes were generated for a fixed range of Kety-Tofts model parameters and for the parameter ranges weighted by estimates from the fully sampled data. The largest-area manifolds determined the optimal three time points for each case. Simulations were performed along with retrospectively subsampled data fits. The agreement was computed between the model parameters fit to three points and those fit to all points. RESULTS: The optimal three-point sample times were from the data-informed simplicial complex analysis and determined to be 65, 204, and 393 s after arrival of the contrast agent to breast tissue. In the patient data, tumor-median parameter values fit using all points and the three selected time points agreed with concordance correlation coefficients of 0.97 for Ktrans and 0.67 for ve . CONCLUSION: It is possible to accurately estimate pharmacokinetic parameters from three properly selected time points inserted into a clinical DCE-MRI breast exam. This technique can provide guidance on when to capture images for quantitative data between high-spatial-resolution DCE-MRI images.

Model-constrained reconstruction accelerated with Fourier-based undersampling for hyperpolarized [1-13 C] pyruvate imaging.

PURPOSE: Model-constrained reconstruction with Fourier-based undersampling (MoReFUn) is introduced to accelerate the acquisition of dynamic MRI using hyperpolarized [1-13 C]-pyruvate. METHODS: The MoReFUn method resolves spatial aliasing using constraints introduced by a pharmacokinetic model that describes the signal evolution of both pyruvate and lactate. Acceleration was evaluated on three single-channel data sets: a numerical digital phantom that is used to validate the accuracy of reconstruction and model parameter restoration under various SNR and undersampling ratios, prospectively and retrospectively sampled data of an in vitro dynamic multispectral phantom, and retrospectively undersampled imaging data from a prostate cancer patient to test the fidelity of reconstructed metabolite time series. RESULTS: All three data sets showed successful reconstruction using MoReFUn. In simulation and retrospective phantom data, the restored time series of pyruvate and lactate maintained the image details, and the mean square residual error of the accelerated reconstruction increased only slightly (< 10%) at a reduction factor up to 8. In prostate data, the quantitative estimation of the conversion-rate constant of pyruvate to lactate was achieved with high accuracy of less than 10% error at a reduction factor of 2 compared with the conversion rate derived from unaccelerated data. CONCLUSION: The MoReFUn technique can be used as an effective and reliable imaging acceleration method for metabolic imaging using hyperpolarized [1-13 C]-pyruvate.

Potential and Challenges in Application of Physiologically Based Pharmacokinetic Modeling in Predicting Diarrheal Disease Impact on Oral Drug Pharmacokinetics.

Physiologically based pharmacokinetic (PBPK) modeling is a physiologically relevant approach that integrates drug-specific and system parameters to generate pharmacokinetic predictions for target populations. It has gained immense popularity for drug-drug interaction, organ impairment, and special population studies over the past two decades. However, an application of PBPK modeling with great potential remains rather overlooked - prediction of diarrheal disease impact on oral drug pharmacokinetics. Oral drug absorption is a complex process involving the interplay between physicochemical characteristics of the drug and physiological conditions in the gastrointestinal tract. Diarrhea, a condition common to numerous diseases impacting many worldwide, is associated with physiological changes in many processes critical to oral drug absorption. In this review, we outline key processes governing oral drug absorption, provide a high-level overview of key parameters for modeling oral drug absorption in PBPK models, examine how diarrheal diseases may impact these processes based on literature findings, illustrate the clinical relevance of diarrheal disease impact on oral drug absorption, and discuss the potential and challenges of applying PBPK modeling in predicting disease impacts. Significance Statement Statement Pathophysiological changes resulting from diarrheal diseases can alter important factors governing oral drug absorption, contributing to suboptimal drug exposure and treatment failure. Physiologically based pharmacokinetic (PBPK) modeling is an in silico approach that has been increasingly adopted for drug-drug interaction potential, organ impairment, and special population assessment. This minireview highlights the potential and challenges of using PBPK modeling as a tool to improve our understanding of how diarrheal diseases impact oral drug pharmacokinetics.