Use of Preclinical Data for Selection of a Phase II/III Dose for Evernimicin and Identification of a Preclinical MIC Breakpoint

ABSTRACT One of the most challenging issues in the design of phase II/III clinical trials of antimicrobial agents is dose selection. The choice is often based on preclinical data from pharmacokinetic (PK) studies with animals and healthy volunteers but is rarely linked directly to the target organisms except by the MIC, an in vitro measure of antimicrobial activity with many limitations. It is the thesis of this paper that rational dose-selection decisions can be made on the basis of the pharmacodynamics (PDs) of the test agent predicted by a mathematical model which uses four data sets: (i) the distribution of MICs for clinical isolates, (ii) the distribution of the values of the PK parameters for the test drug in the population, (iii) the PD target(s) developed from animal models of infection, and (iv) the protein binding characteristics of the test drug. In performing this study with the new anti-infective agent evernimicin, we collected a large number (n = 4,543) of recent clinical isolates of gram-positive pathogens (Streptococcus pneumoniae,Enterococcus faecalis and Enterococcus faecium, and Staphylococcus aureus) and determined the MICs using E-test methods (AB Biodisk, Stockholm, Sweden) for susceptibility to evernimicin. Population PK data were collected from healthy volunteers (n = 40) and patients with hypoalbuminemia (n = 12), and the data were analyzed by using NPEM III. PD targets were developed with a neutropenic murine thigh infection model with three target pathogens: S. pneumoniae (n = 5), E. faecalis(n = 2), and S. aureus (n= 4). Drug exposure or the ratio of the area under the concentration-time curve/MIC (AUC/MIC) was found to be the best predictor of microbiological efficacy. There were three possible microbiological results: stasis of the initial inoculum at 24 h (107 CFU), log killing (pathogen dependent, ranging from 1 to 3 log10), or 90% maximal killing effect (90%E max). The levels of protein binding in humans and mice were similar. The PK and PD of 6 and 9 mg of evernimicin per kg of body weight were compared; the population values for the model parameters and population covariance matrix were used to generate five Monte Carlo simulations with 200 subjects each. The fractional probability of attaining the three PD targets was calculated for each dose and for each of the three pathogens. All differences in the fractional probability of attaining the target AUC/MIC in this PD model were significant. For S. pneumoniae, the probability of attaining all three PD targets was high for both doses. For S. aureus and enterococci, there were increasing differences between the 6- and 9-mg/kg evernimicin doses for reaching the 2 log killing (S. aureus), 1 log killing (enterococci), or 90%E max AUC/MIC targets. This same approach may also be used to set preliminary in vitro MIC breakpoints.

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786 Dose selection for DuoBody®-PD-L1×4-1BB (GEN1046) using a semimechanistic pharmacokinetics/pharmacodynamics model that leverages preclinical and clinical data

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.786 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A821-A821

Author(s):

Gaurav Bajaj ◽

Fereshteh Nazari ◽

Marc Presler ◽

Craig Thalhauser ◽

Ulf Forssmann ◽

...

Keyword(s):

T Cell ◽

Bispecific Antibody ◽

Receptor Occupancy ◽

Model Parameters ◽

Cell Trafficking ◽

Dynamic Binding ◽

Dose Selection ◽

Trimer Formation

BackgroundDuoBody-PD-L1×4-1BB (GEN1046) is a class-defining bispecific antibody, designed to elicit an anti-tumor immune response by simultaneous and complementary blockade of PD-L1 on tumor cells and conditional stimulation of 4-1BB on T-cells and NK cells. Optimizing target engagement for a bispecific antibody is challenging, as it involves binding with two targets, and predicting trimer levels in tumors based on affinity of individual arms and target expression. Here we describe a semimechanistic, physiologically based pharmacokinetic/pharmacodynamic (PK/PD) model that predicts a dosing regimen for DuoBody-PD-L1×4-1BB, which results in the formation of maximum levels of a therapeutically active 4-1BB-bispecific antibody-PD-L1 trimolecular complex (trimer), and optimal PD-L1 receptor occupancy (RO).MethodsAn integrated semimechanistic PK/PD model that describes the distribution of DuoBody-PD-L1×4-1BB into central and peripheral compartments and partitioning into tumor/lymph nodes was developed. The model used PK/PD data and physiological parameters from the literature for parameterizations of PD-L1 and 4-1BB expression levels and T-cell trafficking. The model incorporates dynamic binding of DuoBody-PD-L1×4-1BB to its targets to predict trimer formation and RO for PD-L1 in tumors. Model parameters were calibrated to match in vitro PD studies, such as analyses of T-cell proliferation and cytokine release, as well as clinical PK data. Sensitivity to model assumptions were assessed by varying PK/PD parameters, and assessing their impact on trimer formation and PD-L1 RO. The model was subsequently used to explore in vivo trimer levels and PD-L1 RO in tumors at various dosing regimens.ResultsThe model was able to adequately describe the PK of DuoBody-PD-L1×4-1BB in the central compartment. Simulations showed a bell-shaped response for average trimer levels in tumors that peaked at 100 mg every 3 weeks (Q3W), with doses >100 mg resulting in reduced trimer formation. Average PD-L1 receptor occupancy at the 100 mg dose was predicted to be approximately 70% over 21 days and increased at higher doses. Based on these model predictions, and available safety, anti-tumor activity, and PD data from the ongoing GCT1046-01 trial (NCT03917381), 100 mg Q3W was chosen as the expansion dose for further evaluation in Part 2 of the study.ConclusionsThis semimechanistic PK/PD model provides a novel approach for dose selection of bispecific antibodies such as DuoBody-PD-L1×4-1BB, by using preclinical and clinical PK/PD data to predict formation of optimal trimer levels and PD-L1 receptor occupancy.AcknowledgementsThe authors thank Friederike Gieseke and Zuzana Jirakova at BioNTech SE; Kalyanasundaram Subramanian at Applied Biomath LLC for their valuable contributions.Trial RegistrationWritten informed consent, in accordance with principles that originated in the Declaration of Helsinki 2013, current ICH guidelines including ICH-GCP E6(R2), applicable regulatory requirements, and sponsor policy, was provided by the patients.

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Novel Method To Assess Antiretroviral Target Trough Concentrations UsingIn VitroSusceptibility Data

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.00691-12 ◽

2012 ◽

Vol 56 (11) ◽

pp. 5938-5945 ◽

Cited By ~ 31

Author(s):

Edward P. Acosta ◽

Kay L. Limoli ◽

Lan Trinh ◽

Neil T. Parkin ◽

Jennifer R. King ◽

...

Keyword(s):

Protein Binding ◽

Serum Protein ◽

Drug Exposure ◽

Integrase Inhibitor ◽

Drug Susceptibility ◽

Serum Protein Binding ◽

Drug Concentrations ◽

Antiretroviral Drug

ABSTRACTDurable suppression of HIV-1 replication requires the establishment of antiretroviral drug concentrations that exceed the susceptibility of the virus strain(s) infecting the patient. Minimum plasma drug concentrations (Ctrough) are correlated with response, but determination of targetCtroughvalues is hindered by a paucity ofin vivoconcentration-response data. In the absence of these data,in vitrosusceptibility measurements, adjusted for serum protein binding, can provide estimations of suppressivein vivodrug concentrations. We derived serum protein binding correction factors (PBCF) for protease inhibitors, nonnucleoside reverse transcriptase inhibitors, and an integrase inhibitor by measuring the effect of a range of human serum concentrations onin vitrodrug susceptibility measured with the PhenoSense HIV assay. PBCFs corresponding to 100% HS were extrapolated using linear regression and ranged from 1.4 for nevirapine to 77 for nelfinavir. Using the mean 95% inhibitory concentration (IC95) for ≥1,200 drug-susceptible viruses, we calculated protein-bound IC95(PBIC95) values. PBIC95values were concordant with the minimum effectiveCtroughvalues that were established in well-designed pharmacodynamic studies (e.g., indinavir, saquinavir, and amprenavir). In other cases, the PBIC95values were notably lower (e.g., darunavir, efavirenz, and nevirapine) or higher (nelfinavir and etravirine) than existing target recommendations. The establishment of PBIC95values as described here provides a convenient and standardized approach for estimation of the minimum drug exposure that is required to maintain viral suppression and prevent the emergence of drug-resistant variants, particularly whenin vivoconcentration-response relationships are lacking.

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Phase II Dose Selection for Alpha Synuclein–Targeting Antibody Cinpanemab (BIIB054) Based on Target Protein Binding Levels in the Brain

CPT Pharmacometrics & Systems Pharmacology ◽

10.1002/psp4.12538 ◽

2020 ◽

Vol 9 (9) ◽

pp. 515-522

Author(s):

Mita Kuchimanchi ◽

Michael Monine ◽

Kumar Kandadi Muralidharan ◽

Caroline Woodward ◽

Natalia Penner

Keyword(s):

Protein Binding ◽

Phase Ii ◽

Alpha Synuclein ◽

Target Protein ◽

Dose Selection ◽

Selection For ◽

The Brain

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EVALUATION OF TISSUE STEROID BINDING IN VITRO

Acta Endocrinologica ◽

10.1530/acta.0.068s223 ◽

1971 ◽

Vol 68 (1_Suppl) ◽

pp. S223-S246 ◽

Cited By ~ 2

Author(s):

C. R. Wira ◽

H. Rochefort ◽

E. E. Baulieu

Keyword(s):

Protein Binding ◽

Binding Sites ◽

Experimental System ◽

Specific Protein ◽

Coupling Mechanism ◽

Target Tissue ◽

Protein Binding Sites ◽

Definition Of ◽

Hormone Specificity

ABSTRACT The definition of a RECEPTOR* in terms of a receptive site, an executive site and a coupling mechanism, is followed by a general consideration of four binding criteria, which include hormone specificity, tissue specificity, high affinity and saturation, essential for distinguishing between specific and nonspecific binding. Experimental approaches are proposed for choosing an experimental system (either organized or soluble) and detecting the presence of protein binding sites. Techniques are then presented for evaluating the specific protein binding sites (receptors) in terms of the four criteria. This is followed by a brief consideration of how receptors may be located in cells and characterized when extracted. Finally various examples of oestrogen, androgen, progestagen, glucocorticoid and mineralocorticoid binding to their respective target tissues are presented, to illustrate how researchers have identified specific corticoid and mineralocorticoid binding in their respective target tissue receptors.

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