A Bispecific Modeling Framework Enables the Prediction of Efficacy, Toxicity, and Optimal Molecular Design of Bispecific Antibodies Targeting MerTK.

Inhibiting MerTK on macrophages is a promising therapeutic strategy for augmenting anti-tumor immunity. However, blocking MerTK on retinal pigment epithelial cells (RPEs) results in retinal toxicity. Bispecific antibodies (bsAbs) containing an anti-MerTK therapeutic and anti-PD-L1 targeting arm were developed to reduce drug binding to MerTK on RPEs, since PD-L1 is overexpressed on macrophages but not RPEs. In this study, we present a modeling framework using in vitro receptor occupancy (RO) and pharmacokinetics (PK) data to predict efficacy, toxicity, and therapeutic index (TI) of anti-MerTK bsAbs. We first used simulations and in vitro RO data of anti-MerTK monospecific antibody (msAb) to estimate the required MerTK RO for in vivo efficacy and toxicity. Using these estimated RO thresholds, we employed our model to predict the efficacious and toxic doses for anti-MerTK bsAbs with varying affinities for MerTK. Our model predicted the highest TI for the anti-MerTK/PD-L1 bsAb with an attenuated MerTK binding arm, which was consistent with in vivo efficacy and toxicity observations. Subsequently, we used the model, in combination with sensitivity analysis and parameter scans, to suggest an optimal molecular design of anti-MerTK bsAb with the highest predicted TI in humans. Our prediction revealed that this optimized anti-MerTK bsAb should contain a MerTK therapeutic arm with relatively low affinity, along with a high affinity targeting arm that can bind to a low abundance target with slow turnover rate. Overall, these results demonstrated that our modeling framework can guide the rational design of bsAbs.

A Potent Pan-TGFß Neutralizing Monoclonal Antibody Elicits Cardiovascular Toxicity in Mice and Cynomolgus Monkeys.

Transforming growth factor ß (TGFß) signaling has been recently shown to reduce antitumor response to PD-L1 blockade, leading to a renewed enthusiasm in developing anti-TGFß therapies for potential combination with cancer immunotherapy agents. Inhibition of TGFß signaling in nonclinical toxicology species is associated with serious adverse toxicities including cardiac valvulopathies and anemia. Previously, cardiovascular toxicities have been thought to be limited to small molecule inhibitors of TGFß receptor and not considered to be a liability associated with pan-TGFß neutralizing monoclonal antibodies (mAbs). Here, we report the toxicity findings associated with a potent pan-TGFß neutralizing mAb (pan-TGFß mAb; neutralizes TGFß1, 2, and 3) after 5 weekly intravenous doses of 10, 30, and 100 mg/kg, followed by a 4-week recovery period, in mice and cynomolgus monkeys. Mortality was observed due to acute bleeding and cardiovascular toxicity in mice at ≥ 30 mg/kg and prolonged menstruation in female monkeys at 100 mg/kg. Additional findings considered to be on-target exaggerated pharmacology included generalized bleeding and cardiovascular toxicity in mice and monkeys; histopathologic changes in the teeth, tongue, and skin in mice; and abnormal wound healing and microscopic pathology in the bone in monkeys. Importantly, our data indicate that the cardiovascular toxicities associated with the inhibition of TGFß signaling are not limited to small molecule inhibitors but are also observed following administration of a potent pan-TGFß inhibiting mAb.

Effects of renal impairment on transporter-mediated renal reabsorption of drugs and renal drug-drug interactions: A simulation-based study.

Renal impairment (RI) significantly impacts the clearance of drugs through changes in the glomerular filtration rate, protein binding and alterations in the expression of renal drug transport proteins and hepatic metabolizing enzymes. The objectives of this study were to evaluate quantitatively the effects of renal impairment on the pharmacokinetics of drugs undergoing renal transporter-mediated reabsorption. A previously published semi-mechanistic kidney model incorporating physiologically relevant fluid reabsorption and transporter-mediated active renal reabsorption (PMID: 26341876) was utilized in this study. The probe drug/transporter pair utilized was γ-hydroxybutyric acid (GHB) and monocarboxylate transporter 1 (SCL16A1, MCT1). γ-Hydroxybutyric acid concentrations in the blood and amount excreted into urine were simulated using ADAPT 5 for the i.v. dose range of 200-1500 mg/kg in rats and the impact of renal impairment on CLR and AUC was evaluated. A 90% decrease in GFR resulted in a > 100-fold decrease in GHB CLR . When expression of reabsorptive transporters was decreased and fu was increased, CLR approached GFR. The effect of renal impairment on CLR was reduced when the expression of drug metabolizing enzymes (DME) was increased as a result of increased metabolic clearance; the converse held true when the DME expression was decreased. In conclusion, this study quantitatively demonstrated that the effects of renal insufficiency on the clearance of drugs is modulated by transporter expression, contribution of renal clearance to overall clearance, expression of drug metabolizing enzymes, fraction unbound and drug-drug interactions with inhibitors of renal transporters that may be increased in the presence of renal impairment.

γ-Hydroxybutyric Acid (GHB) Pharmacokinetics and Pharmacodynamics: Semi-Mechanistic and Physiologically Relevant PK/PD Model.

An overdose of γ-hydroxybutyric acid (GHB), a drug of abuse, results in fatality caused by severe respiratory depression. In this study, a semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was developed to characterize monocarboxylate transporter 1 (MCT1)-mediated transport of GHB, as well as effects of GHB on respiration frequency, for IV doses of 200, 600, and 1500 mg/kg in rats. The proposed PK/PD model for GHB consists of nonlinear metabolism of GHB in the liver, MCT1-mediated renal reabsorption with physiologically relevant concurrent fluid reabsorption, MCT1-mediated uptake into the brain, and direct effects of binding of GHB to GABAB receptors on the PD parameter, respiration frequency. Michaelis-Menten affinity constants for metabolism, renal reabsorption, and uptake into and efflux from the brain were fixed to the observed in vitro values. The IC 50 value for the effect of GHB on respiration frequency was fixed to a reported value for binding of GHB to GABAB receptors. All physiological parameters were fixed to the reported values for a 300-g rat. The model successfully captured the GHB PK/PD data and was further validated using the data for a 600-mg/kg dose of GHB after IV bolus administration. Unbound GHB brain ECF/blood partition coefficient (Kp u,u ) values obtained from the model agreed well with values calculated using experimental ECF concentrations obtained with brain microdialysis, demonstrating the physiological relevance of this model. Sensitivity analysis indicated that the PK/PD model was stable. In conclusion, we developed a semi-mechanistic and physiologically relevant PK/PD model of GHB using in vitro drug-transporter kinetics and in vivo PK/PD data in rats.

Novel high/low solubility classification methods for new molecular entities.

This research describes a rapid solubility classification approach that could be used in the discovery and development of new molecular entities. Compounds (N=635) were divided into two groups based on information available in the literature: high solubility (BDDCS/BCS 1/3) and low solubility (BDDCS/BCS 2/4). We established decision rules for determining solubility classes using measured log solubility in molar units (MLogSM) or measured solubility (MSol) in mg/ml units. ROC curve analysis was applied to determine statistically significant threshold values of MSol and MLogSM. Results indicated that NMEs with MLogSM>-3.05 or MSol>0.30mg/mL will have ≥85% probability of being highly soluble and new molecular entities with MLogSM≤-3.05 or MSol≤0.30mg/mL will have ≥85% probability of being poorly soluble. When comparing solubility classification using the threshold values of MLogSM or MSol with BDDCS, we were able to correctly classify 85% of compounds. We also evaluated solubility classification of an independent set of 108 orally administered drugs using MSol (0.3mg/mL) and our method correctly classified 81% and 95% of compounds into high and low solubility classes, respectively. The high/low solubility classification using MLogSM or MSol is novel and independent of traditionally used dose number criteria.

A quantitative threshold for high/low extent of urinary excretion of compounds in humans.

In this study, a quantitative threshold was determined for the high/low extent of urinary excretion (UE) of compounds in humans, using a straightforward but robust statistical method known as receiver operating characteristic curve (ROC) analysis, and also 18 potential physicochemical determinants of UE were evaluated. Data on the percent of drug excreted unchanged into the urine, %Ae , were used to determine the threshold for high/low UE. Compounds can be divided into high/low UE groups using the threshold value of Ae  = 16.8%, namely those with Ae  > 16.8% are classified as high UE and those with Ae  ≤ 16.8% as low UE. The %Ae negatively correlated with cLogP (r = -0.56); however, cLogP could not quantitatively predict the value of %Ae (R(2) adj. = 0.32). Several determinants of the extent of UE, including cLogP, ACD labs cLogP and ACD labs cLogD(pH=7.4) , were successfully evaluated as a priori indicators of the extent of UE using two cut-off values for each parameter. Moreover, 87% of the 90 compounds in the external validation set were correctly classified using this approach. Analysis of the physicochemical spaces of compounds in these two groups showed significant overlap, which hinders the a priori determination of the extent of UE of compounds using a single threshold/cut-off value of simple physicochemical parameters. In conclusion, 16.8% is a quantitative threshold value to distinguish between high and low UE and new molecular entities with cLogP and ACD labs cLogP values of ≤0.7 and ≥1.0 and ACD labs cLogD(pH=7.4) values of ≤0.0 and ≥0.5 could be identified as exhibiting high and low UE, respectively. Copyright © 2016 John Wiley & Sons, Ltd.

Semi-mechanistic kidney model incorporating physiologically-relevant fluid reabsorption and transporter-mediated renal reabsorption: pharmacokinetics of γ-hydroxybutyric acid and L-lactate in rats.

This study developed a semi-mechanistic kidney model incorporating physiologically-relevant fluid reabsorption and transporter-mediated active reabsorption. The model was applied to data for the drug of abuse γ-hydroxybutyric acid (GHB), which exhibits monocarboxylate transporter (MCT1/SMCT1)-mediated renal reabsorption. The kidney model consists of various nephron segments--proximal tubules, Loop-of-Henle, distal tubules, and collecting ducts--where the segmental fluid flow rates, volumes, and sequential reabsorption were incorporated as functions of the glomerular filtration rate. The active renal reabsorption was modeled as vectorial transport across proximal tubule cells. In addition, the model included physiological blood, liver, and remainder compartments. The population pharmacokinetic modeling was performed using ADAPT5 for GHB blood concentration-time data and cumulative amount excreted unchanged into urine data (200-1000 mg/kg IV bolus doses) from rats [Felmlee et al (PMID: 20461486)]. Simulations assessed the effects of inhibition (R = [I]/KI = 0-100) of renal reabsorption on systemic exposure (AUC) and renal clearance of GHB. Visual predictive checks and other model diagnostic plots indicated that the model reasonably captured GHB concentrations. Simulations demonstrated that the inhibition of renal reabsorption significantly increased GHB renal clearance and decreased AUC. Model validation was performed using a separate dataset. Furthermore, our model successfully evaluated the pharmacokinetics of L-lactate using data obtained from Morse et al (PMID: 24854892). In conclusion, we developed a semi-mechanistic kidney model that can be used to evaluate transporter-mediated active renal reabsorption of drugs by the kidney.

Quantitative structure-pharmacokinetic relationships for the prediction of renal clearance in humans.

Renal clearance (CLR), a major route of elimination for many drugs and drug metabolites, represents the net result of glomerular filtration, active secretion and reabsorption, and passive reabsorption. The aim of this study was to develop quantitative structure-pharmacokinetic relationships (QSPKR) to predict CLR of drugs or drug-like compounds in humans. Human CLR data for 382 compounds were obtained from the literature. Step-wise multiple linear regression was used to construct QSPKR models for training sets and their predictive performance was evaluated using internal validation (leave-one-out method). All qualified models were validated externally using test sets. QSPKR models were also constructed for compounds in accordance with their 1) net elimination pathways (net secretion, extensive net secretion, net reabsorption, and extensive net reabsorption), 2) net elimination clearances (net secretion clearance, CLSEC; or net reabsorption clearance, CLREAB), 3) ion status, and 4) substrate/inhibitor specificity for renal transporters. We were able to predict 1) CLREAB (Q(2) = 0.77) of all compounds undergoing net reabsorption; 2) CLREAB (Q(2) = 0.81) of all compounds undergoing extensive net reabsorption; and 3) CLR for substrates and/or inhibitors of OAT1/3 (Q(2) = 0.81), OCT2 (Q(2) = 0.85), MRP2/4 (Q(2) = 0.78), P-gp (Q(2) = 0.71), and MATE1/2K (Q(2) = 0.81). Moreover, compounds undergoing net reabsorption/extensive net reabsorption predominantly belonged to Biopharmaceutics Drug Disposition Classification System classes 1 and 2. In conclusion, constructed parsimonious QSPKR models can be used to predict CLR of compounds that 1) undergo net reabsorption/extensive net reabsorption and 2) are substrates and/or inhibitors of human renal transporters.

Pharmacokinetics and pharmacodynamics of phenethyl isothiocyanate: implications in breast cancer prevention.

Phenethyl isothiocyanate (PEITC)-a naturally occurring isothiocyanate in cruciferous vegetables-has been extensively studied as a chemopreventive agent in several preclinical species and in humans. Pharmacokinetic features of unchanged PEITC are (I) linear and first-order absorption, (II) high protein binding and capacity-limited tissue distribution, and (III) reversible metabolism and capacity-limited hepatic elimination. Membrane transport of PEITC is mediated by BCRP, multidrug resistance-associated protein (MRP) 1, and MRP2 transporters belonging to the ATP-binding-cassette (ABC) family. PEITC is metabolized by glutathione S-transferase (GST) in the liver, with the glutathione conjugate of PEITC undergoing further conversion to mercapturic acid by N-acetyl transferase in rats and humans. PEITC modulates the activity and expression of numerous phase I and phase II drug-metabolizing enzymes and can inhibit the metabolism of procarcinogens to form carcinogens and increase carcinogen elimination. In recent years, several in vitro and in vivo studies have elucidated molecular mechanisms underlying the pharmacodynamics of PEITC in breast cancer that include cancer cell apoptosis by upregulation of apoptotic genes, cell cycle arrest at G2/M phase by generation of reactive oxygen species and depletion of intracellular glutathione, downregulation of the estrogen receptor, decrease in sensitivity to estrogen, and inhibition of tumor metastasis. Inhibition of angiogenesis is one of the recently reported mechanisms of breast cancer prevention by PEITC. Complex pharmacokinetics and pharmacodynamics of PEITC necessitate a systems-biology approach in parallel with PK/PD modeling to develop PEITC as a therapeutic agent for treating cancers.

Mechanistic models describing active renal reabsorption and secretion: a simulation-based study.

The objective of the present study was to evaluate mechanistic pharmacokinetic models describing active renal secretion and reabsorption over a range of Michaelis-Menten parameter estimates and doses. Plasma concentration and urinary excretion profiles were simulated and renal clearance (CL(r)) was calculated for two pharmacokinetic models describing active renal reabsorption (R1/R2), two models describing active secretion (S1/S2), and a model containing both processes. A range of doses (1-1,000 mg/kg) was evaluated, and V (max) and K (m) parameter estimates were varied over a 100-fold range. Similar CL(r) values were predicted for reabsorption models (R1/R2) with variations in V (max) and K (m). Tubular secretion models (S1/S2) yielded similar relationships between Michaelis-Menten parameter perturbations and CL(r), but the predicted CL(r) values were threefold higher for model S1. For both reabsorption and secretion models, the greatest changes in CL(r) were observed with perturbations in V (max), suggesting the need for an accurate estimate of this parameter. When intrinsic clearance was substituted for Michaelis-Menten parameters, it failed to predict similar CL(r) values even within the linear range. For models S1 and S2, renal secretion was predominant at low doses, whereas renal clearance was driven by fraction unbound in plasma at high doses. Simulations demonstrated the importance of Michaelis-Menten parameter estimates (especially V (max)) for determining CL(r). K (m) estimates can easily be obtained directly from in vitro studies. However, additional scaling of in vitro V (max) estimates using in vitro/in vivo extrapolation methods are required to incorporate these parameters into pharmacokinetic models.