Mechanistic PK/PD modeling to address early-stage biotherapeutic dosing feasibility questions

Figures & data

Table 1. Summary of examples for applications of mechanistic PK/PD models.

Reference	Model	Analysis	Conclusions	Impact
Is there an optimal target within a pathway? Is it better to target the ligand or the receptor?
Dwivedi et al. 2014^Citation10	mAb inhibiting IL6-dependent signaling, IL6 binding to IL6Rα, gp130, soluble IL6Rα, soluble gp130, downstream STAT3 signaling linked to CRP	Prospective analysis to determine optimal MoA	Antibody with dual mechanism targeting both IL6 and IL6:soluble IL6Rα shows the best promise compared to other MoAs such as binding to IL6 or IL6Rα alone	Support for MOA and lead selection criteria
Tiwari et al. 2016^Citation11	mAb inhibiting IL13-dependent signaling, IL13 binding to receptor subunits IL4Ra, IL13Rα1, IL13Rα2	Retrospective analysis to determine best MoA between two clinical candidates	Antibody targeting IL13 without interfering with IL13Rα1 and IL13Rα2 shows better potency despite weaker affinity	Support for MOA and lead selection criteria
Kondic et al. 2022^Citation12	Anti-tryptase antibodies, formation of active, but short-lived tryptase tetramer from inactive, stable monomer	Prospective analysis to compare antibody binding to tetrameric vs. monomeric form of tryptase	Antibody that binds to both active and inactive forms and induce dissociation of tetramer, even with weak affinity, will perform better than one binding only to the active tetramer form	Support for MOA and lead selection criteria
Is it feasible to achieve intended pharmacology in the clinic?
ABM Case Study	Antagonistic anti-CD47 antibody	Retrospective analysis of clinical data	Even at the optimal antibody Kd, high and frequent dosing is required for anti-CD47 therapies	Flag dose feasibility as a high risk for this program
Kondic et al. 2022^Citation12	Non-antagonistic anti-GDF15 antibody	Prospective analysis of MOA for new target	Concept to increase total GDF15 to therapeutic levels through antibody binding was not feasible (limited by protein synthesis rates)	Support for early no-go decision
ABM Case Study	T-cell engager, Solitomab binding to EpCAM and CD3	Retrospective analysis of clinical data and opportunity for best-in-class	Model predicted small TI based on trimer formation in tumor and tox tissue	Support for early no-go and/or risk mitigation
What are the required drug properties to achieve desired pharmacology at reasonable doses?
Tiwari et al. 2017^Citation13	mAb targeting soluble and/or membrane targets	Broad analysis of hypothetical targets, benchmarked against marketed therapies	Antibody Kd requirements dependent on target expression and turnover	Support for lead identification criteria
Kapitanov et al. 2021^Citation14	Site-of-action PK/PD model of anti-CCL20 antibody	Retrospective of clinical data- support for competitive differentiation	Optimal Kd for lead optimization is 35 pM	Support for lead identification criteria
Yu et al. 2011^Citation15	mAb targeting CMV, viral dynamics	Prospective analysis of anti-viral antibody based on known viral infection dynamics	Optimal Kd is between 30–100 pM	Support for lead identification criteria

Figure 1. Schematic of how mechanistic PK/PD models can support drug discovery and development across the pipeline. Different data can be leveraged at each stage (text above the model diagram) to build a mechanistic PK/PD model that can be used to answer questions specific to the stage of development (text below the model diagram). Established early, then updated continuously, the model can seamlessly support decisions from early discovery to clinical. Figure created with BioRender.Com.

A process diagram depicting the different stages of drug discovery and development. Example mechanistic PK/PD model diagram is at center. Model inputs available at each stage are shown feeding into the model, and model outputs are shown flowing out of the model.

Figure 2. Mechanistic PK/PD model analysis of magrolimab. A) model diagram of the monospecific anti-receptor with membrane ligand competitor (4-compartment) model from Applied BioMath Assess™ used for simulating PK, target engagement and target inhibition of magrolimab. B) Simulations of 7 doses of magrolimab, dosed at 3 to 50 mg/kg IV Q1W. Left panel is simulated drug concentration vs. time in central compartment, middle panel is target engagement vs. time in tumor (disease compartment), and right panel is target inhibition vs. time in tumor. C) from the simulations in B), the dose vs. trough target engagement and trough target inhibition in the tumor is plotted. Doses predicted to achieve 95% target engagement or inhibition are indicated.

3 Panel figure. Figure A depicts the model diagram for the Monospecific Anti-Receptor with Competitive Membrane Ligand Biotherapeutic Model. Model species are identified by icons and reactions by arrows. Figure B shows line plots of total soluble drug concentration in central compartment versus time; target engagement, as a percentage, in the disease compartment versus time; target inhibition, as a percentage, versus time for different doses of drug. Figure C shows a line plot of percent target engagement or target inhibition versus dose and identifies 9.37 milligram per kilogram as the dose corresponding to 95% target engagement and 23.7 milligram per kilogram as the dose corresponding to 95% target inhibition.

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2(a) diagram contains three small boxes arranged in a column to the right of the image which are labeled, from top to bottom, “peripheral”, “disease” and “toxicity”. These boxes represent compartments that make up the structure of a model.For each box there are two arrows on the left side which lead into another larger compartment which is positioned to the left of the first three. This larger compartment is labeled “Central”. Dose administration is depicted as an arrow into the larger “Central”compartment. A separate box contains an image of the reactions that is connected to the other “compartments” in the model and describes model reactions that are occurring there. The species in the model are antibody, membrane ligand, receptor, and shed receptor, each depicted by a distinct icon. Binding reactions, depicted by arrows, describe the formation of 5 different complexes: antibody to 1 shed receptor on a binding arm, antibody to 2 shed receptors, antibody to 1 receptor on a binding arm, antibody to 2 receptors, and antibody to 1 receptor on one arm and 1 shed receptor on the other arm. Binding of membrane ligand to shed receptor or receptor are also depicted as arrows. Synthesis and degradation of model species are depicted as one-sided arrows.

Figure 3. Mechanistic PK/PD model analysis of solitomab. A) model diagram of the T cell engager for solid tumors model in Applied BioMath Assess™ used for simulating solitomab PK and trimer formation in the tumor and tox compartments. B) from these simulations, the dose vs. mean trimer in the tumor and tox compartments are plotted. The dose predicted to result in 500 trimers/cell in each compartment is indicated.  

2 panel figure. Figure A depicts the model diagram for the T cell Engager for Solid Tumors model. Model species are identified by icons and reactions by arrows. Figure B shows a line plot of mean trimer per T cell, in units of molecules per cell, versus dose, in units of milligram, for the tumor and tox compartments. The plot identifies 13.5 micrograms per day as the dose corresponding to 500 mean trimers per T cell in the tumor compartment, and 28 micrograms per day as the dose corresponding to 500 mean trimers per T cell in the tox compartment.

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3(a) diagram contains 4 compartments represented by boxes arranged in a 2 × 2 grid. The boxes are labeled “Central”(top left), “Tumor” (top right), “Peripheral” (bottom left)and “Tox” (bottom right). The species in the model are a T-cell engager depicted with two different binding arms, receptor expressed on tumor cells, receptor expression on normal cells, and a T cell receptor expressed on T cells. Receptor on normal cells are depicted in central, peripheral, and tox compartments. Receptor on tumor cells are depicted in the tumor compartment. Reversible binding of theT-cell engager to receptor on normal or tumor cell, reversible binding of T-cell engager to T-cell receptor expressed on T cells, and reversible binding to form tumor or normal cell – T-cell engager- T-cell complexes are depicted as bi-directional arrows. Transport between the central compartment and the peripheral, tumor, or tox compartments are depicted as bidirectional arrows.

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