A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat.

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

Physiologically based modelling of dermal absorption and kinetics of consumer-relevant chemicals: A case study with exposure to bisphenol A from thermal paper.

Bisphenol A (BPA) is one of the best studied industrial chemicals in terms of exposure, toxicity, and toxicokinetics. This renders it an ideal candidate to exploit the recent advancements in physiologically based pharmacokinetic (PBPK) modelling to support risk assessment of BPA specifically, and of other consumer-relevant hazardous chemicals in general. Using the exposure from thermal paper as a case scenario, this study employed the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) model available in the Simcyp® Simulator to simulate the dermal toxicokinetics of BPA at local and systemic levels. Sensitivity analysis helped to identify physicochemical and physiological factors influencing the systemic exposure to BPA. The iterative modelling process was as follows: (i) development of compound files for BPA and its conjugates, (ii) setting-up of a PBPK model for intravenous administration, (iii) extension for oral administration, and (iv) extension for exposure via skin (i.e., hand) contact. A toxicokinetic study involving hand contact to BPA-containing paper was used for model refinement. Cumulative urinary excretion of total BPA had to be employed for dose reconstruction. PBPK model performance was verified using the observed serum BPA concentrations. The predicted distribution across the skin compartments revealed a depot of BPA in the stratum corneum (SC). These findings shed light on the role of the SC to act as temporary reservoir for lipophilic chemicals prior to systemic absorption, which inter alia is relevant for the interpretation of human biomonitoring data and for establishing the relationship between external and internal measures of exposure.

Intramuscular cabotegravir and rilpivirine concentrations after switching from efavirenz-containing regimen.

AIMS: Intramuscular cabotegravir/rilpivirine (IM CAB/RPV) are metabolized by UGT1A1/CYP3A4. Efavirenz induces both enzymes; therefore, switching from an efavirenz-containing regimen to IM CAB/RPV could possibly result in suboptimal levels. Due to their long dosing interval, clinical studies with IM CAB/RPV are challenging. We used physiologically based pharmacokinetics (PBPK) modelling to simulate the switch from efavirenz to IM CAB/RPV. METHODS: First, we developed the drug models and verified the performance of the PBPK model to predict the pharmacokinetics of IM cabotegravir, IM rilpivirine and efavirenz by comparing the simulations against observed clinical data. Second, we verified the ability of the model to predict the effect of residual induction with observed data for the switch from efavirenz to dolutegravir or rilpivirine. Finally, we generated a cohort of 100 virtual individuals (20-50 years, 50% female, 18.5-30 kg/m2 ) to simulate IM CAB/RPV concentrations after discontinuing efavirenz in extensive and slow metabolizers of efavirenz. RESULTS: IM CAB concentrations were predicted to decrease by 11% (95% confidence interval 7-15%), 13% (6-21%) and 8% (0-18%) at day 1, 7 and 14 after efavirenz discontinuation. CAB concentrations were predicted to remain above the minimal efficacy threshold (i.e., 664 ng/mL) throughout the switch period both in extensive and slow metabolizers of efavirenz. Similarly, IM RPV concentrations were modestly decreased with the lowest reduction being 10% (6-14%) on day 7 post last efavirenz dose. CONCLUSION: Our simulations indicate that switching from an efavirenz-containing regimen to IM CAB/RPV does not put at risk of having a time window with suboptimal drug levels.

Assessing Dose-Exposure-Response Relationships of Miltefosine in Adults and Children using Physiologically-Based Pharmacokinetic Modeling Approach.

OBJECTIVES: Miltefosine is the first and only oral medication to be successfully utilized as an antileishmanial agent. However, the drug is associated with differences in exposure patterns and cure rates among different population groups e.g. ethnicity and age (i.e., children v adults) in clinical trials. In this work, mechanistic population physiologically-based pharmacokinetic (PBPK) models have been developed to study the dose-exposure-response relationship of miltefosine in in silico clinical trials and evaluate the differences in population groups, particularly children and adults. METHODS: The Simcyp population pharmacokinetics platform was employed to predict miltefosine exposure in plasma and peripheral blood mononuclear cells (PBMCs) in a virtual population under different dosing regimens. The cure rate of a simulation was based on the percentage of number of the individual virtual subjects with AUCd0-28 > 535 µg⋅day/mL in the virtual population. RESULTS: It is shown that both adult and paediatric PBPK models of miltefosine can be developed to predict the PK data of the clinical trials accurately. There was no significant difference in the predicted dose-exposure-response of the miltefosine treatment for different simulated ethnicities under the same dose regime and the dose-selection strategies determined the clinical outcome of the miltefosine treatment. A lower cure rate of the miltefosine treatment in paediatrics was predicted because a lower exposure of miltefosine was simulated in virtual paediatric in comparison with adult virtual populations when they received the same dose of the treatment. CONCLUSIONS: The mechanistic PBPK model suggested that the higher fraction of unbound miltefosine in plasma was responsible for a higher probability of failure in paediatrics because of the difference in the distribution of plasma proteins between adults and paediatrics. The developed PBPK models could be used to determine an optimal miltefosine dose regime in future clinical trials.

Physiologically based pharmacokinetic modelling to predict drug-drug interactions for encorafenib. Part I. Model building, validation, and prospective predictions with enzyme inhibitors, inducers, and transporter inhibitors.

Encorafenib, a potent BRAF kinase inhibitor undergoes significant metabolism by CYP3A4 (83%) and CYP2C19 (16%) and also a substrate of P-glycoprotein (P-gp). Because of this, encorafenib possesses potential for enzyme-transporter related interactions. Clinically, its drug-drug interactions (DDIs) with CYP3A4 inhibitors (posaconazole, diltiazem) were reported and hence there is a necessity to study DDIs with multiple enzyme inhibitors, inducers, and P-gp inhibitors.USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors, CYP3A4 inducers were selected and prospective DDIs were simulated using physiologically based pharmacokinetic modelling (PBPK). Impact of dose (50 mg vs. 300 mg) and staggering of administrations (0-10 h) on the DDIs were predicted.PBPK models for encorafenib, perpetrators simulated PK parameters within twofold prediction error. Clinically reported DDIs with posaconazole and diltiazem were successfully predicted.CYP2C19 inhibitors did not result in significant DDI whereas strong CYP3A4 inhibitors resulted in DDI ratio up to 4.5. Combining CYP3A4, CYP2C19 inhibitors yielded DDI equivalent CYP3A4 alone. Strong CYP3A4 inducers yielded DDI ratio up to 0.3 and no impact of P-gp inhibitors on DDIs was observed. The DDIs were not impacted by dose and staggering of administration. Overall, this work indicated significance of PBPK modelling for evaluating clinical DDIs with enzymes, transporters and interplay.

Physiologically based pharmacokinetic modeling (PBPK) to predict drug-drug interactions for encorafenib. Part II. Prospective predictions in hepatic and renal impaired populations with clinical inhibitors and inducers.

Encorafenib, a potent BRAF kinase inhibitor gets significantly metabolised by CYP3A4 (83%) and CYP2C19 (16%) and is a substrate for P-glycoprotein (P-gp). Due to significant metabolism by CYP3A4, encorafenib exposure can increase in hepatic and renal impairment and may lead to altered magnitude of drug-drug interactions (DDI). Hence, it is necessary to assess the exposures & DDI's in impaired population.Physiologically based pharmacokinetic modelling (PBPK) was utilised to determine the exposures of encorafenib in hepatic and renal impairment along with altered DDI's. Prospective DDI's were predicted with USFDA recommended clinical CYP3A4, CYP2C19, P-gp inhibitors and CYP3A4 inducers.PBPK models for encorafenib, perpetrators simulated PK parameters within 2-folds error. Encorafenib exposures significantly increased in hepatic as compared to renal impairment because of reduced CYP3A4 levels.Hepatic impairment caused changes in inhibition and induction DDI's, when compared to healthy population. Renal impairment did not cause significant changes in DDIs except for itraconazole. P-gp, CYP2C19 inhibitors did not result in altered DDI's. The DDI's were found to have insignificant correlation with relative exposure increase of perpetrators in case of impairment. Overall, this work signifies use of PBPK modelling for DDI's evaluations in hepatic and renal impairment populations.

Meeting report: DMDG peptide and oligonucleotide ADME workshop 2022.

Challenges within peptide and oligonucleotide ADME (absorption, distribution, metabolism and elimination) and scientific ideas on how to solve them were presented and discussed at the DMDG (Drug Metabolism and Discussion Group) Peptide and Oligonucleotide ADME Workshop 2022 (2nd and 3rd of October 2022). This meeting report summarises the presentations and discussions from this workshop.The following topics were covered:Overview of the drug modality landscapeMetabolism & modellingAnalytical challengesDrug-drug interactions reports from industry working groupsRegulatory interactions.

Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans.

The increasing intentional and non-intentional exposure to nanoparticles (NPs) has raised the interest concerning their fate and biodistribution in the body of animals and humans after inhalation. In this context, Physiologically Based (pharmaco)Kinetic (PBK) modelling has emerged as an in silico approach that simulates the biodistribution kinetics of NPs in the body using mathematical equations. Due to restrictions in data availability, such models are first developed for rats or mice. In this work, we present the interspecies extrapolation of a PBK model initially developed for rats, in order to estimate the biodistribution of inhaled gold NPs (AuNPs) in humans. The extrapolation framework is validated by comparing the model results with experimental data from a clinical study performed on humans for inhaled AuNPs of two different sizes, namely 34 nm and 4 nm. The novelty of this work lies in the extrapolation of a PBK model for inhaled AuNPs to humans and comparison with clinical data. The extrapolated model is in good agreement with the experimental data, and provides insights for the mechanisms of inhaled AuNP translocation to the blood circulation, after inhalation. Finally, the biodistribution of the two sizes of AuNPs in the human body after 28 days post-exposure is estimated by the model and discussed.

Differences in Pharmacokinetics and Haematotoxicities of Aniline and Its Dimethyl Derivatives Orally Administered in Rats.

Aniline and its dimethyl derivatives reportedly become haematotoxic after metabolic N-hydroxylation of their amino groups. The plasma concentrations of aniline and its dimethyl derivatives after single oral doses of 25 mg/kg in rats were quantitatively measured and semi-quantitatively estimated using LC-tandem mass spectrometry. The quantitatively determined elimination rates of aniline; 2,4-dimethylaniline; and 3,5-dimethylaniline based on rat plasma versus time curves were generally rapid compared with those of 2,3-; 2,5-; 2,6-; and N,2-dimethylaniline. The primary acetylated metabolites of aniline; 2,4-dimethylaniline; and 3,5-dimethylaniline, as semi-quantitatively estimated based on their peak areas in LC analyses, were more extensively formed than those of 2,3-; 2,5-; 2,6-; and N,2-dimethylaniline. The areas under the curve of unmetabolized (remaining) aniline and its dimethyl derivatives estimated using simplified physiologically based pharmacokinetic models (that were set up using the experimental plasma concentrations) showed an apparently positive correlation with the reported lowest-observed-effect levels for haematotoxicity of these chemicals. In the case of 2,4-dimethylaniline, a methyl group at another C4-positon would be one of the determinant factors for rapid metabolic elimination to form aminotoluic acid. These results suggest that rapid and extensive metabolic activation of aniline and its dimethyl derivatives occurred in rats and that the presence of a methyl group at the C2-positon may generally suppress fast metabolic rates of dimethyl aniline derivatives that promote metabolic activation reactions at NH2 moieties.

Hepatotoxicological potential of P-toluic acid in humanised-liver mice investigated using simplified physiologically based pharmacokinetic models.

p-Toluic acid, a metabolite of organic solvent xylene, has a high reported no-observed-effect level (NOEL, 1000 mg/kg) in rats, possibly because of direct glycine conjugation to methylhippuric acid. In this study, plasma levels of p-toluic acid and its glycine conjugate in mice and humanised-liver mice were evaluated after oral administrations.Although rapid conversion of p-toluic acid to its glycine conjugate was evident from mouse plasma concentrations, the biotransformation of p-toluic acid was slower in humanised-liver mice. The input parameters for physiologically based pharmacokinetic (PBPK) models were determined using fitting procedures to create PBPK-generated plasma concentration curves.The PBPK-modelled hepatic concentrations of p-toluic acid in humanised-liver mice were higher than those observed in plasma. PBPK-modelled hepatic and plasma concentrations of p-toluic acid also indicated slow elimination in humans.These results suggest that rapid conjugations of p-toluic acid reportedly observed in rats could result in overestimation of NOELs for conjugatable chemicals when extrapolated to humanised-liver mice or humans.

In vitro and In silico biopharmaceutic regulatory guidelines for generic bioequivalence for oral products: Comparison among various regulatory agencies.

Generic drug development is a complex process that involves development of formulation similar to reference product. Because of the complexity associated with generic drug development, many regulatory agencies have come up with various guidelines. Out of many guidelines, the biopharmaceutics classification system that was introduced in 1995 based on aqueous solubility and permeability helped many pharmaceutical scientists across the globe to utilize the tool for formulation development, waiver of in vivo studies. Later on in vitro guidelines based on dissolution and in vitro in vivo correlation were introduced by many regulatory agencies with an intent to reduce number of in vivo human testing thereby facilitating shorter development time and faster approvals and launch. Most recently, understanding the importance in silico approaches such as physiologically based pharmacokinetic modelling, regulatory agencies such as United States Food and Drug Administration (USFDA) and European Middle East and Africa (EMA) came up with modelling guidance documents. Even though consensus exists between guidance documents from various regulatory agencies, still there are many minor to major differences exists between these guidance documents that needs to be considered while submitting a generic drug application. This review aims to compare all the in vitro and in silico guidance documents from major regulatory agencies with emphasis on latest trends and technologies combined with regulatory acceptability with an intention to harmonize regulations. Guidance documents from major regulatory agencies such as USFDA, EMA, World Health Organization, International Council for Harmonization and other emerging markets were compared. Similarities &differences among these guidance documents are critically reviewed to provide the reader a detailed overview of these guidance documents at one place.

Negative Food Effect of Danirixin: Use of PBPK Modelling to Explore the Effect of Formulation and Meal Type on Clinical PK.

PURPOSE: To use physiologically-based pharmacokinetic (PBPK) modelling to explore the food effect of different DNX hydrobromide (HBr) hemihydrate salt tablet formulations using biorelevant dissolution. METHODS: Compendial dissolution using a paddle method and TIM-1 biorelevant dissolution were performed and incorporated into a previously reported PBPK model. A two-part clinical study evaluated tablet formulations in the fasted/fed (high fat) state (Part A), and the impact of food (fasted/normal/high fat) and Proton Pump Inhibitor (PPI) co-administration for a selected formulation; as well as a formulation containing DNX HBr in the monohydrate state (Part B). RESULTS: TIM-1 data showed that the fed state bioaccessibility of DNX was significantly decreased compared to the fasted state with no significant differences between formulations. Dosed with normal/high fat food the selected formulation showed comparable exposure and a modest increase in DNX systemic PK was observed with PPI dependent on meal type. Under fed conditions DNX systemic exposure was comparable for the monohydrate and hemihydrate formulations. The integration of biorelevant TIM-1 data into the PBPK model led to the successful simulation of a DNX negative food effect. CONCLUSIONS: Interactions between DNX and food components are the likely the source of the negative food effect via micellar entrapment, ion pairing and/or meal induced viscosity changes.

Using pharmacokinetic modelling to improve prescribing practices of intravenous aminophylline in childhood asthma exacerbations.

OBJECTIVE: To evaluate physiologically based pharmacokinetic modelling (PBPK) software in paediatric asthma patients using intravenous aminophylline. METHODS: Prospective clinical audit of children receiving iv aminophylline (July 2014 to June 2016), and in-silico modelling using Simcyp software. RESULTS: Thirty-eight admissions (25 children) were included. Children with aminophylline levels ≥10 mg/l had equivalent clinical outcomes compared to those <10 mg/L, and adverse effects occurred in 57%. Therapeutic drug monitoring (TDM) data correlated well with PBPK model. PBPK modelling of a 5 mg/kg iv loading dose (≤18yr) shows a mean Cmax of 8.99 mg/L (5th-95th centiles 5.5-13.7 mg/L), with 70.3% of subjects <10 mg/L, 29.4% achieving 10-20 mg/L, and 0.1% > 20 mg/L. For an aminophylline infusion (0-12 y) of 1.0  mg/kg/h, the mean steady state infusion concentration was 16.4 mg/L, (5th-95th centiles 5.3-32 mg/L), with 26.8% having a serum concentration >20 mg/L. For 12-18yr receiving 0.5  mg/kg/h infusion, the mean steady state infusion concentration was 9.37 mg/L (5th-95th centiles 3.4-18 mg/L), with 59.8% having a serum concentration <10 mg/L. CONCLUSION: PBPK software modelling correlates well with clinical data. Current aminophylline iv loading dosage recommendations achieve levels <10 mg/l in 70% of children. Routine TDM may need altering as low risk of toxicity (>20 mg/l).

Investigating the effect of autoinduction in cynomolgus monkeys of a novel anticancer MDM2 antagonist, idasanutlin, and relevance to humans.

1. Idasanutlin (RG7388) is a potent p53-MDM2 antagonist currently in clinical development for treatment of cancer. The purpose of the present studies was to investigate the cause of marked decrease in plasma exposure after repeated oral administration of RG7388 in monkeys and whether the autoinduction observed in monkeys is relevant to humans. 2. In monkey liver and intestinal microsomes collected after repeated oral administration of RG7388 to monkeys, significantly increased activities of homologue CYP3A8 were observed (ex vivo). Investigation using a physiologically based pharmacokinetic (PBPK) model suggested that the loss of exposure was primarily due to induction of metabolism in the gut of monkeys. 3. Studies in monkey and human primary hepatocytes showed that CYP3A induction by RG7388 only occurred in monkey hepatocytes but not in human hepatocytes, which suggests the observed CYP3A induction is monkey specific. 4. The human PK data obtained from the first cohorts confirmed the lack of relevant induction as predicted by the human hepatocytes and the PBPK modelling based on no induction in humans.

Physiologically-based pharmacokinetic modelling of immune, reproductive and carcinogenic effects from contaminant exposure in polar bears (Ursus maritimus) across the Arctic.

Polar bears (Ursus maritimus) consume large quantities of seal blubber and other high trophic marine mammals and consequently have some of the highest tissue concentrations of organohalogen contaminants (OHCs) among Arctic biota. In the present paper we carried out a risk quotient (RQ) evaluation on OHC-exposed polar bears harvested from 1999 to 2008 and from 11 circumpolar subpopulations spanning from Alaska to Svalbard in order to evaluate the risk of OHC-mediated reproductive effects (embryotoxicity, teratogenicity), immunotoxicity and carcinogenicity (genotoxicity). This RQ evaluation was based on the Critical Body Residue (CBR) concept and a Physiologically-Based Pharmacokinetic Modelling (PBPK) approach using OHC concentrations measured in polar bear adipose or liver tissue. The range of OHC concentrations within polar bear populations were as follows for adipose, sum polychlorinated biphenyls ∑PCBs (1797-10,537 ng/g lw), sum methylsulphone-PCB ∑MeSO2-PCBs (110-672 ng/g lw), sum chlordanes ∑CHLs (765-3477 ng/g lw), α-hexachlorocyclohexane α-HCH (8.5-91.3 ng/g lw), ß-hexachlorocyclohexane ß-HCH (65.5-542 ng/g lw), sum chlorbenzenes ∑ClBzs (145-304 ng/g lw), dichlorodiphenyltrichloroethane ∑DDTs (31.5-206 ng/g lw), dieldrin (69-249 ng/g lw), polybrominated diphenyl ethers ∑PBDEs (4.6-78.4 ng/g lw). For liver, the perfluorooctanesulfonic acid (PFOS) concentrations ranged from 231-2792 ng/g ww. The total additive RQ from all OHCs ranged from 4.3 in Alaska to 28.6 in East Greenland bears for effects on reproduction, immune health and carcinogenicity, highlighting the important result that the toxic effect threshold (i.e. RQ>1) was exceeded for all polar bear populations assessed. PCBs were the main contributors for all three effect categories, contributing from 70.6% to 94.3% of the total risk and a RQ between 3.8-22.5. ∑MeSO2-PCBs were the second highest effect contributor for reproductive and immunological effects (0.17

Prediction of in vivo drug performance using in vitro dissolution coupled with STELLA: a study with selected drug products.

Prediction of the in vivo performance of the drug product from the in vitro studies is the major challenging job for the pharmaceutical industries. From the current regulatory perspective, biorelevant dissolution media should now be considered as quality control media in order to avoid the risk associated. Physiological based pharmacokinetic models (PBPK) coupled with biorelevant dissolution medium is widely used in simulation and prediction of the plasma drug concentration and in vivo drug performance. The present investigation deals with the evaluation of biorelevant dissolution media as well as in vivo drug performance by PBPK modelling using STELLA® simulation software. The PBPK model was developed using STELLA® using dissolution kinetics, solubility, standard gastrointestinal parameters and post-absorptive disposition parameters. The drug product selected for the present study includes Linezolid film-coated immediate-release tablets (Zyvox), Tacrolimus prolonged-release capsules (Advagraf), Valganciclovir tablets (Valcyte) and Mesalamine controlled-release capsules (Pentasa) each belonging to different biopharmaceutics classification system (BCS). The simulated plasma drug concentration was analyzed and pharmacokinetic parameters were calculated and compared with the reported values. The result from the present investigation indicates that STELLA® when coupled with biorelevant dissolution media can predict the in vivo performance of the drug product with prediction error less than 20% irrespective of the dosage form (immediate release versus modified release) and BCS Classification. Thus, STELLA® can be used for in vivo drug prediction which will be helpful in generic drug development.

At the bench: the key role of PK-PD modelling in enabling the early discovery of biologic therapies.

Pharmacokinetic-pharmacodynamic (PK-PD) modelling is already used extensively in pre-clinical and clinical drug development to characterize drug candidates quantitatively, aid go/no-go decisions and to inform future trial design and optimal dosing regimens. Less well known, although arguably as powerful, is its application at the earliest stages of drug development, at target selection and lead selection, where these same techniques can be used to predict and so bring forward drug candidates with the necessary characteristics or, for unachievable requirements, allow the abandonment of the programme for the minimum spend of time and cost. We consider three examples that illustrate the power of the application of modelling at this early stage. We start with the simple case of determining the optimal characteristics for a monoclonal antibody against a soluble ligand with its application to the investment decision for the development of best-in-class compounds. This is extended to the more complex situation of the target protein having an endogenous, inhibitory binding protein. We then illustrate how using physiologically-based pharmacokinetic modelling enables the appropriate engineering and testing of biological therapeutics for optimal PK-PD characteristics. These examples illustrate how a minimal investment in modelling achieves orders of magnitude better returns in choosing the correct targets, mechanism of action and candidate characteristics to progress to clinical trials, streamlining drug development and delivering better medicines to patients.

The effect of malaria-induced alteration of metabolism on piperaquine disposition in Plasmodium yoelii infected mice and predicted in malaria patients.

OBJECTIVES: Malaria-induced alteration of physiological parameters and pharmacokinetic properties of antimalarial drugs may be clinically relevant. Whether and how malaria alters the disposition of piperaquine (PQ) was investigated in this study. METHODS: The effect of malaria on drug metabolism-related enzymes and PQ pharmacokinetic profiles was studied in Plasmodium yoelii-infected mice in vitro/in vivo. Whether the malaria effect was clinically relevant for PQ was evaluated using a validated physiologically-based pharmacokinetic model with malaria-specific scalars obtained in mice. RESULTS: The infection led to a higher blood-to-plasma partitioning (Rbp) for PQ, which was concentration-dependent and correlated to parasitemia. No significant change in plasma protein binding was found for PQ. Drug metabolism-related genes (CYPs/UDP-glucuronosyltransferase/nuclear receptor, except for CYP2a5) were downregulated in infected mice, especially at the acute phase. The plasma oral clearances (CL/F) of three probe substrates for CYP enzymes were significantly decreased (by ≥35.9%) in mice even with moderate infection. The validated physiologically-based pharmacokinetic model indicated that the hepatic clearance (CLH) of PQ was the determinant of its simulated CL/F, which was predicted to slightly decrease (by ≤23.6%) in severely infected mice but not in malaria patients. The result fitted well with the plasma pharmacokinetics of PQ in infected mice and literature data on malaria patients. The blood clearance of PQ was much lower than its plasma clearance due to its high Rbp. CONCLUSIONS: The malaria-induced alteration of drug metabolism was substrate-dependent, and its impact on the disposition of PQ and maybe other long-acting aminoquinoline antimalarials was not expected to be clinically relevant.

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron.

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.

Facing the Facts of Altered Plasma Protein Binding: Do Current Models Correctly Predict Changes in Fraction Unbound in Special Populations?

Accounting for variability in plasma protein binding of drugs is an essential input to physiologically-based pharmacokinetic (PBPK) models of special populations. Prediction of fraction unbound in plasma (fu) in such populations typically considers changes in plasma protein concentration while assuming that the binding affinity remains unchanged. A good correlation between predicted vs observed fu data reported for various drugs in a given special population is often used as a justification for such predictive methods. However, none of these analyses evaluated the prediction of the fold-change in fu in special populations relative to the reference population. This would be a more appropriate assessment of the predictivity, analogous to drug-drug interactions. In this study, predictive performance of the single protein binding model was assessed by predicting fu for alpha-1-acid glycoprotein and albumin bound drugs in hepatic impairment, renal impairment, paediatric, elderly, patients with inflammatory disease, and in different ethnic groups for a dataset of >200 drugs. For albumin models, the concordance correlation coefficients for predicted fu were >0.90 for 16 out of 17 populations with sub-groups, indicating strong agreement between predicted and observed values. In contrast, concordance correlation coefficients for predicted fold-change in fu for the same dataset were <0.38 for all populations and sub-groups. Trends were similar for alpha-1-acid glycoprotein models. Accordingly, the predictions of fu solely based on changes in protein concentrations in plasma cannot explain the observed values in some special populations. We recommend further consideration of the impact of changes in special populations to endogenous substances that competitively bind to plasma proteins, and changes in albumin structure due to posttranslational modifications. PBPK models of special populations for highly bound drugs should preferably use measured fu data to ensure reliable prediction of drug exposure or compare predicted unbound drug exposure between populations knowing that these will not be sensitive to changes in fu.