Tycho Heimbach

Prodrugs: design and clinical applications

Prodrugs are bioreversible derivatives of drug molecules that undergo an enzymatic and/or chemical transformation in vivo to release the active parent drug, which can then exert the desired pharmacological effect. In both drug discovery and development, prodrugs have become an established tool for improving physicochemical, biopharmaceutical or pharmacokinetic properties of pharmacologically active agents. About 5–7% of drugs approved worldwide can be classified as prodrugs, and the implementation of a prodrug approach in the early stages of drug discovery is a growing trend. To illustrate the applicability of the prodrug strategy, this article describes the most common functional groups that are amenable to prodrug design, and highlights examples of prodrugs that are either launched or are undergoing human trials.

Physiologically based pharmacokinetic modeling in drug discovery and development: A pharmaceutical industry perspective

The application of physiologically based pharmacokinetic (PBPK) modeling has developed rapidly within the pharmaceutical industry and is becoming an integral part of drug discovery and development. In this study, we provide a cross pharmaceutical industry position on “how PBPK modeling can be applied in industry” focusing on the strategies for application of PBPK at different stages, an associated perspective on the confidence and challenges, as well as guidance on interacting with regulatory agencies and internal best practices.

Case Studies for Practical Food Effect Assessments across BCS/BDDCS Class Compounds using In Silico, In Vitro, and Preclinical In Vivo Data

Practical food effect predictions and assessments were described using in silico, in vitro, and/or in vivo preclinical data to anticipate food effects and Biopharmaceutics Classification System (BCS)/Biopharmaceutics Drug Disposition Classification System (BDDCS) class across drug development stages depending on available data: (1) limited in silico and in vitro data in early discovery; (2) preclinical in vivo pharmacokinetic, absorption, and metabolism data at candidate selection; and (3) physiologically based absorption modeling using biorelevant solubility and precipitation data to quantitatively predict human food effects, oral absorption, and pharmacokinetic profiles for early clinical studies. Early food effect predictions used calculated or measured physicochemical properties to establish a preliminary BCS/BDDCS class. A rat-based preclinical BCS/BDDCS classification used rat in vivo fraction absorbed and metabolism data. Biorelevant solubility and precipitation kinetic data were generated via animal pharmacokinetic studies using advanced compartmental absorption and transit (ACAT) models or in vitro methods. Predicted human plasma concentration–time profiles and the magnitude of the food effects were compared with observed clinical data for assessment of simulation accuracy. Simulations and analyses successfully identified potential food effects across BCS/BDDCS classes 1–4 compounds with an average fold error less than 1.6 in most cases. ACAT physiological absorption models accurately predicted positive food effects in human for poorly soluble bases after oral dosage forms. Integration of solubility, precipitation time, and metabolism data allowed confident identification of a compound’s BCS/BDDCS class, its likely food effects, along with prediction of human exposure profiles under fast and fed conditions.

A simplified PBPK modeling approach for prediction of pharmacokinetics of four primarily renally excreted and CYP3A metabolized compounds during pregnancy.

During pregnancy, a drug’s pharmacokinetics may be altered and hence anticipation of potential systemic exposure changes is highly desirable. Physiologically based pharmacokinetics (PBPK) models have recently been used to influence clinical trial design or to facilitate regulatory interactions. Ideally, whole-body PBPK models can be used to predict a drug’s systemic exposure in pregnant women based on major physiological changes which can impact drug clearance (i.e., in the kidney and liver) and distribution (i.e., adipose and fetoplacental unit). We described a simple and readily implementable multitissue/organ whole-body PBPK model with key pregnancy-related physiological parameters to characterize the PK of reference drugs (metformin, digoxin, midazolam, and emtricitabine) in pregnant women compared with the PK in nonpregnant or postpartum (PP) women. Physiological data related to changes in maternal body weight, tissue volume, cardiac output, renal function, blood flows, and cytochrome P450 activity were collected from the literature and incorporated into the structural PBPK model that describes HV or PP women PK data. Subsequently, the changes in exposure (area under the curve (AUC) and maximum concentration (Cmax)) in pregnant women were simulated. Model-simulated PK profiles were overall in agreement with observed data. The prediction fold error for Cmax and AUC ratio (pregnant vs. nonpregnant) was less than 1.3-fold, indicating that the pregnant PBPK model is useful. The utilization of this simplified model in drug development may aid in designing clinical studies to identify potential exposure changes in pregnant women a priori for compounds which are mainly eliminated renally or metabolized by CYP3A4.

Pharmacokinetics of dietary cancer chemopreventive compound dibenzoylmethane in rats and the impact of nanoemulsion and genetic knockout of Nrf2 on its disposition

The pharmacokinetic disposition of a dietary cancer chemopreventive compound dibenzoylmethane (DBM) was studied in male Sprague‐Dawley rats after intravenous (i.v.) and oral (p.o.) administrations. Following a single i.v. bolus dose, the mean plasma clearance (CL) of DBM was low compared with the hepatic blood flow. DBM displayed a high volume of distribution (Vss). The elimination terminal t1/2 was long. The mean CL, Vss and AUC0−∞/dose were similar between the i.v. 10 and 10 mg/kg doses. After single oral doses (10, 50 and 250 mg/kg), the absolute oral bioavailability (F*) of DBM was 7.4%–13.6%. The increase in AUC was not proportional to the oral doses, suggesting non‐linearity. In silico prediction of oral absorption also demonstrated low DBM absorption in vivo. An oil‐in‐water nanoemulsion containing DBM was formulated to potentially overcome the low F* due to poor water solubility of DBM, with enhanced oral absorption. Finally, to examine the role of Nrf2 on the pharmacokinetics of DBM, since DBM activates the Nrf2‐dependent detoxification pathways, Nrf2 wild‐type (+/+) mice and Nrf2 knockout (−/−) mice were utilized. There was an increased systemic plasma exposure of DBM in Nrf2 (−/−) mice, suggesting that the Nrf2 genotype could also play a role in the pharmacokinetic disposition of DBM. Taken together, the results show that DBM has low oral bioavailability which could be due in part to poor water solubility and this could be overcome by a nanotechnology‐based drug delivery system and furthermore the Nrf2 genotype could also play a role in the pharmacokinetics of DBM. Copyright

Practical anticipation of human efficacious doses and pharmacokinetics using in vitro and preclinical in vivo data.

Accurate predictions of human pharmacokinetic and pharmacodynamic (PK/PD) profiles are critical in early drug development, as safe, efficacious, and “developable” dosing regimens of promising compounds have to be identified. While advantages of successful integration of preclinical PK/PD data in the “anticipation” of human doses (AHD) have been recognized, pharmaceutical scientists have faced difficulties with practical implementation, especially for PK/PD profile projections of compounds with challenging absorption, distribution, metabolism, excretion and formulation properties. In this article, practical projection approaches for formulation-dependent human PK/PD parameters and profiles of Biopharmaceutics Classification System classes I-IV drugs based on preclinical data are described. Case examples for “AHD” demonstrate the utility of preclinical and clinical PK/PD modeling for formulation risk identification, lead candidate differentiation, and prediction of clinical outcome. The application of allometric scaling methods and physiologically based pharmacokinetic approaches for clearance or volume of distribution projections is described using GastroPlus™. Methods to enhance prediction confidence such as in vitro–in vivo extrapolations in clearance predictions using in vitro microsomal data are discussed. Examples for integration of clinical PK/PD and formulation data from frontrunner compounds via “reverse pharmacology strategies” that minimize uncertainty with PK/PD predictions are included. The use of integrated softwares such as GastroPlus™ in combination with established PK projection methods allow the projection of formulation-dependent preclinical and human PK/PD profiles required for compound differentiation and development risk assessments.

Utility of Physiologically Based Modeling and Preclinical In Vitro/In Vivo Data to Mitigate Positive Food Effect in a BCS Class 2 Compound

Physiologically based pharmacokinetic (PBPK) modeling has become a useful tool to estimate the performance of orally administrated drugs. Here, we described multiple in silico/in vitro/in vivo tools to support formulation development toward mitigating the positive food effect of NVS123, a weak base with a pH-dependent and limited solubility. Administered orally with high-fat meal, NVS123 formulated as dry filled capsules displayed a positive food effects in humans. Three alternative formulations were developed and assessed in in vitro and in vivo preclinical and/or clinical studies. By integrating preclinical in vitro and in vivo data, the PBPK model successfully estimated the magnitude of food effects and the predicted values were within ±30% of the observed results. A model-guided parameter sensitivity analysis illustrated that enhanced solubility and longer precipitation times under fed condition were the main reason for enhanced NVS123s exposure in presence of food. Eventually, exposure after an amorphous formulation was found to be not significantly altered because of remarkably enhanced intestinal solubility and reduced precipitation. Gastroplus population simulations also suggested that the amorphous formulation is promising in mitigating a clinically significant food effect. Overall, these efforts supported the rationale of clinical investigation of the new formulation, and more importantly, highlighted a practical application of PBPK modeling solving issues of undesirable food effects in weakly basic compounds based on preclinical in vitro/in vivo data.

Prospective Predictions of Human Pharmacokinetics for Eighteen Compounds

Quantitative predictions of pharmacokinetics (PKs) and concentration-time profiles using in vitro and in vivo preclinical data are critical to estimate systemic exposures for first-in-human studies. Prospective prediction accuracies of human PKs for 18 compounds across all Biopharmaceutics Classification System/Biopharmaceutics Drug Disposition Classification System classes were evaluated. The a priori predicted profiles were then compared with clinical profiles. Predictions were conducted using advanced compartmental absorption and transit (ACAT) physiology based PK models. Human intravenous profiles were predicted with in vivo preclinical intravenous data using Wajima formulas. Human oral profiles were generated by combining intravenous PKs together with either physiologically based oral ACAT models utilizing solubility and permeability data or by using the average bioavailability (F) and absorption rate constant (ka ) from preclinical species. Key PK parameters evaluated were the maximum plasma concentration (Cmax ), the area under the plasma concentration-time curve (AUC), CL/F, and Vdss /F. A decision tree was provided to guide human PK and ACAT predictions. Our prospective human PK prediction methods yielded good prediction results. The predictions were within a twofold error for 80% (Cmax ), 65% (AUC), 65% (CL/F), and 80% (Vz /F) of the compounds. The methods described can be readily implemented with available in vitro and in vivo data during early drug development.

Nilotinib preclinical pharmacokinetics and practical application toward clinical projections of oral absorption and systemic availability

Nilotinib is a highly potent and selective bcr‐abl tyrosine kinase inhibitor used for the treatment of patients who are in the chronic and accelerated phases of Philadelphia chromosome‐positive (Ph+) chronic myeloid leukemia (CML). Nilotinib preclinical data and its use for practical predictions of systemic exposure profiles and oral absorption are described. The systemic clearance (CL) of nilotinib was relatively low in rodents with a value of less than 25% of hepatic blood flow (QH), while it was moderate in monkeys and dogs (CL /QH = 32–35%). The steady state volume of distribution (Vss) ranged from 0.55 to 3.9 l/kg across the species tested. The maximum concentration (Cmax) of nilotinib occurred at 0.5–4 h and the bioavailability was moderate (17–44%). The plasma protein binding was high (> 97.5%) in preclinical species and humans. The human CL (~ 0.1 l/h/kg) and Vss (~2.0 l/kg) were best predicted by the rat–dog–human proportionality method and allometric scaling method, respectively. The human intravenous pharmacokinetic profile was projected by the Wajima ‘Css‐MRT’ method. The predicted micro‐constants from human intravenous profiles were incorporated into the advanced compartmental absorption and transit model within the GastroPlus program to simulate the oral concentration–time curves in humans. Overall, the simulated oral human pharmacokinetic profiles showed good agreement with observed clinical data, and the model predicted that the Cmax, AUC, t1/2, Vz/F and CL/F values were within 1.3‐fold of the observed values. The absolute oral bioavailability of nilotinib in healthy humans was predicted to be low (< 25%). Copyright

IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes

&NA; Three Physiologically Based Pharmacokinetic software packages (GI‐Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded “bottom‐up” anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. Foral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water‐soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug‐specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data “as is” in this blinded bottom‐up prediction approach. Graphical Abstract Figure. No caption available.