Development of A Physiologically Based Pharmacokinetic (PBPK) Model for

Simulating the Disposition of Antibody-Drug Conjugates

Ke Szeto, Haiying Zhou

kexu@simulations-plus.com

haiying@simulations-plus.com

Simulations Plus, Inc

6/20/2016

Outline

Objectives

Introduction of Target-mediated Drug Disposition model (TMDD) for mAbs (unconjugated antibody or ADC) in a PBPK setting

Case studies: model simulation of ADC PK in preclinical animals after intravenous administration (tumor-free and tumor bearing animals)

Conclusion 2

Objectives

• Physiologically based PK (PBPK) modeling of small molecules has been successfully applied in drug discovery and development – Identify problem compounds in discovery

– Enable inter-species scaling, FIH, estimate tissue concentrations

– Virtual trials in large populations……

• Quantification of ADC (or mAbs) distribution and elimination is more complicated – Large molecular size and poor membrane permeability

– Elimination occurs in tissues throughout the body

– Binding affinity of ADC to their targets

– A mixture of drug-to-antibody ratio (DAR) moieties

• PBPK modeling of ADCs, mAbs and payloads can help to predict tissue concentrations and to understand disposition of all these molecules in different organs

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PBPK Modeling of mAbs

PBPK

Drug Specific

Parameters

Physiological Parameters

Mwt Vascular reflection coefficient Lymph reflection coefficient FcRn binding Antigen binding etc…

Age Species Gender weight

Organ size Tissue composition Blood perfusion rate Lymph flow rate FcRn concentration etc…

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Antibody Drug Conjugate Structure

5 Christina Peters, Bioscience Reports Jul 14, 2015, 35 (4)

Modelling Antibody-Drug Conjugate • Distribution and elimination

processes of the multiple ADC species with different DAR (drug to antibody ratio)

• Distribute to the peripheral compartment

• Cleared by non-specific clearance

• Bind to the target receptor, internalize and be cleared in the cell lysosome

• Release and clear of toxin molecule

• Toxin molecules can fall off the antibody due to deconjugation

• Toxin molecules attached to the antibody are released and be distributed in the body when ADCs clearance occurs, both through non-specific mechanism and specific-target binding

• Released toxin will be cleared through metabolism or renally

Two Models for Deconjugation

A series of transit processes to describe the deconjugation process from higher to lower DARs

DARi

DARi-1

DAR1

DAR0

The average DAR used to describe the deconjugation of conjugated ADC to unconjugated antibody

Mean DAR

(mixture of

DARi, DARi-

1, … DAR0)

DAR0

Provides simulation results of total antibody, conjugated antibody, conjugated drug, unconjugated antibody, and unconjugated drug

Provides simulation results of total antibody, mean DAR, unconjugated antibody, conjugated drug, and unconjugated drug

Convective Transport Model Transport from vascular to interstitial space: (1 − 𝜎𝑣) × 𝐶𝑣 × 𝐿

Transport from interstitial space to lymph: (1 − 𝜎𝐿) × 𝐶𝑖 × 𝐿

Parameters and variables: L: the organ lymph flow Cv: the vascular drug concentration Ci: the interstitial drug concentration σv : the vascular reflection coefficient σL : the lymph reflection coefficient

𝜎𝑣 = 𝜎𝑣,𝑚𝑢𝑠𝑐𝑙𝑒 × 𝑓𝑙𝑒𝑎𝑘𝑎𝑔𝑒; 𝑓𝑙𝑒𝑎𝑘𝑎𝑔𝑒 < 1

𝜎𝑣,𝑚𝑢𝑠𝑐𝑙𝑒 + 1 − 𝜎𝑣,𝑚𝑢𝑠𝑐𝑙𝑒 × 𝑓𝑙𝑒𝑎𝑘𝑎𝑔𝑒 − 1 ; 𝑓𝑙𝑒𝑎𝑘𝑎𝑔𝑒 ≥ 1

Tissue vascular reflection coefficient calculation based on its relative vascular leakage level (a physiological property):

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Lymph Flow • For human, lymph flows at around 125mL/h, and this rate may be

increased 10-fold during exercise (Stucker et al. 2008).

(*Lindena et al. 1986)

Default tissue lymph flow in GastroPlus set to 0.4% of tissue plasma flow.

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Fluid Phase Endocytosis Endosomal uptake: ADCs can be taken up into the endosomal compartment from the vascular and interstitial spaces via fluid phase endocytosis:

Parameters: 𝑅1: endocytosis uptake rate constant 𝑉𝑒𝑛𝑑𝑜: volume of the endosomal space FR: fraction of the rate constant related to the vascular space

Uptake rate = 𝑅1 × FR × 𝐶𝑣 × 𝑉𝑒𝑛𝑑𝑜 + 𝑅1 × (1 − FR) × 𝐶𝑖 × 𝑉𝑒𝑛𝑑𝑜

Recycling: • Late endosomes fuse back to the endothelial cell membrane • ADC-FcRn complexes dissociate and ADC recycles back to vascular and interstitial spaces

Recycling rate = 𝑅2 × 𝐶𝑒𝑛𝑑𝑜−3𝑓

× 𝑉𝑒𝑛𝑑𝑜 Parameters: 𝑅2: recycling rate constant

𝐶𝑒𝑛𝑑𝑜−3𝑓

: free mAb concentration in the last endosomal sub-compartment

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FcRn Binding and Degradation

1. IgG uptake by endocytosis

2. pH-dependent binding of IgG to FcRn in endosome.

1. pH ~7.4 low affinity

2. pH ~ 6.0 high affinity

3. Bound IgG is transported with the FcRn receptor back to the cell surface where it is released

4. Unbound IgG is degraded in lysosomes (pH ~4.5)

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FcRn Binding • The activity of vacuolar ATPase acidifies

the endosomal space (Lafourcade et al. 2008)

• ADCs (or mAbs) may have higher binding affinity to FcRn at this acidic pH ~ 6.0.

• The bound ADC is recycled to the cell surface with FcRn where it dissociates

• The unbound ADC is degraded in lysosomes

ADC and FcRn association rate = 𝐾𝑜𝑛 𝑝𝐻 𝐹𝑐𝑅𝑛𝑇𝑜𝑡 − 𝐶𝑒𝑛𝑑𝑜−𝑖𝑏 × 𝐶𝑒𝑛𝑑𝑜−𝑖

𝑓× 𝑉𝑒𝑛𝑑𝑜−𝑖

ADC-FcRn complexes dissociation rate = 𝐾𝑜𝑓𝑓 𝑝𝐻 × 𝐶𝑒𝑛𝑑𝑜−𝑖𝑏 × 𝑉𝑒𝑛𝑑𝑜−𝑖

Parameters: 𝐾𝑜𝑛 𝑝𝐻 and 𝐾𝑜𝑓𝑓 𝑝𝐻 : the association and dissociation rate constants at specific pH

𝐹𝑐𝑅𝑛𝑇𝑜𝑡: FcRn concentration in a given organ

𝐶𝑒𝑛𝑑𝑜−𝑖𝑓

and 𝐶𝑒𝑛𝑑𝑜−𝑖𝑏 : free and bound mAb concentrations in endosomal sub-compartment i

𝑉𝑒𝑛𝑑𝑜−𝑖: volume of endosomal sub-compartment I τ: transit time between endosomal sub-compartments

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FcRn Expression • Expressed in the vascular endothelial cells of various cells (Urva et al. 2010)

• The estimated whole-body averaged concentration, 1.66 mM, was assigned as the FcRn concentration of muscle tissue.

• FcRn concentrations in other tissues were calculated based on the relative expression of FcRn mRNA (Chen and Balthasar, 2012)

(M)

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At baseline steady state:

𝐾𝑠𝑦𝑛,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 = 𝐾𝑑𝑒𝑔,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 × 𝐶𝑎𝑛𝑡𝑖𝑔𝑒𝑛,𝑡=0

The dynamic of the antigen express level:

𝑉𝑖𝑑𝐶𝑎𝑛𝑡𝑖𝑔𝑒𝑛

𝑑𝑡= 𝐾𝑠𝑦𝑛,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 × 𝑉𝑖 − 𝐾𝑑𝑒𝑔,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 × 𝐶𝑎𝑛𝑡𝑖𝑔𝑒𝑛 − 𝐶𝑖

𝑏 × 𝑉𝑖 − 𝐾𝑖𝑛𝑡,𝑇𝑀𝐷 × 𝐶𝑖𝑏 × 𝑉𝑖

ADC and antigen association rate = 𝐾𝑜𝑛,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 × 𝐶𝑎𝑛𝑡𝑖𝑔𝑒𝑛 − 𝐶𝑖𝑏 × 𝐶𝑖

𝑓× 𝑉𝑖

ADC-antigen complex dissociation rate = 𝐾𝑜𝑓𝑓,𝑎𝑛𝑡𝑖𝑔𝑒𝑛 × 𝐶𝑖𝑏 × 𝑉𝑖

Target Mediated Drug Disposition (TMDD) Model

Antigen Concentration:

ADC(or mAb) – Antigen binding:

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Mechanisms Included • Transport of mAb into the tissue interstitial space via convective flow

through the paracellular pores in the vascular endothelium (one-pore formalism)

• Uptake of mAb from the vascular and interstitial compartment into the endosomal compartment via fluid phase endocytosis

• pH dependent binding of mAb to FcRn in the endosomal compartment

• FcRn binding competition between therapeutic mAb and endogenous IgG

• Recycling of mAb to the vascular and to the interstitial compartments

• Endosomal degradation of the unbound mAb

• Return of mAb from the tissue to the bloodstream through convective transport with lymph flow

• Specific mAb binding with antigen

• ADC deconjugation

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Case Study I: IV Injection of Trastuzumab Emtansine (T-DM1)

ADC details:

• Trastuzumab: humanized anti-HER2 monoclonal IgG antibody

• DM1: potent derivative of the microtubule inhibitor maytansine

• Linker: a stable nonreducible (“noncleavable”) thioether bond using the SMCC (N-succinimidyl-4-(N-maleimidomethyl)-cyclohexane-1-carboxylate) linker

Dose information:

• 10 mg/kg IV T-DM1 with mean DAR as 1.5 in rats

• 10 mg/kg IV T-DM1 with mean DAR as 3.1 in rats

• 30 mg/kg IV T-DM1 with mean DAR as 3.1 in cynomolgus monkey;

[1] Bender et al., AAPS J, 16 (2014), 994-1008;

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Case Study I: IV Injection of Trastuzumab Emtansine (T-DM1)

Model Settings:

Parameters Values Unit Source

DAR 1.5 distribution 1% DAR5, 4% DAR4, 13% DAR3, 26% DAR2, 35%DAR1, and 21% DAR0

NA [1]

DAR 3.1 distribution 2% DAR7, 5% DAR6, 10% DAR5, 19% DAR4, 26% DAR3, 23% DAR2, 13% DAR1, and 2% DAR0

NA [1]

Kon,FcRn(6.0) for monkey (exogenous)

3000 1/uM/day Fitted

Kdeconj 0.05

1/day Fitted with DAR=1.5 in rat and keep constant

[1] Bender et al., AAPS J, 16 (2014), 994-1008;

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Results: Averaged DAR 1.5 in Rats

Figure 1: Comparison of simulated (lines) and measured (points)

plasma concentrations of total antibody and various DAR conjugates for

10 mg/kg T-DM1 dose with averaged DAR=1.5 distributions in rats.

Total mAb DAR=2

DAR=3

DAR=4

DAR=0

DAR=1

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Results: Averaged DAR 3.1 in Rats

DAR=4

DAR=5

DAR=6

DAR=7

DAR=0

DAR=1

DAR=2

DAR=3

Total mAb

Figure 2: Comparison of simulated (lines) and measured (points)

plasma concentrations of total antibody and various DAR conjugates for

10 mg/kg T-DM1 dose with averaged DAR = 3.1 in rats.

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Results: Averaged DAR 3.1 in Monkeys

Total mAb DAR=4

DAR=5

DAR=6

DAR=7

DAR=0

DAR=1

DAR=2

DAR=3

Figure 3: Comparison of simulated (lines) and measured (points) plasma concentrations of total antibody and various DAR conjugates for 10 mg/kg T-DM1 dose with averaged DAR = 3.1 in monkeys.

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Case Study 2: IV Injection of anti-5T4 ADC A1mcMMAF

[1] Shah et al., AAPS J, 16 (2014), 452-463

ADC details:

• Humanized anti-5T4 antibody (A1)

• Potent microtubule-disrupting agent monomethylauristatin F (MMAF)

• a noncleavable maleimidocaproyl (mc) linker

Dose information:

• 1 mg/kg and 10 mg/kg IV A1mcMMAF with mean DAR=4 in tumor-free mouse

• 3 mg/kg IV A1mcMMAF with mean DAR=4 in H1975 or DYT2 tumor bearing mice (human tumor xenograft)

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Case Study 2: IV Injection of anti-5T4 ADC A1mcMMAF

Parameters Values Unit Source

Kon,Antigen 23.2 1/nM/day [1]

Koff,Antigen 21.6 1/day [1]

Kint,Ag 4.2 1/day [1]

Vtumor 0.5 mL [1]

PStc in tumor 5E-6 mL/s Fitted (account for binding)

Kon,FcRn(6.0) 800 1/uM/day Fitted

AgTotal (H1975) 104 nM [1]

AgTotal (MDA-MB-361/DYT2)

66.4

nM [1]

CLint,PL 10 L/h Fitted

Kdeconj 0.3 1/d Fitted

Model Parameters:

[1] Shah et al., AAPS J, 16 (2014), 452-463

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Results: 1mgk and 10mgk control mice

Total mAb

Free mAb

Payload

Total mAb

Free mAb

Payload

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Results: tumor-bearing mice

Total mAb

Payload-plasma

Payload-tumor

Total mAb

Payload-plasma

Payload-tumor

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Conclusion

• The PBPK modeling of ADCs (or mAbs) in GastroPlusTM can accurately simulate:

– The plasma and tissue concentrations of different species of ADCs (with different DARs)

– The tissue and plasma concentrations of the payload in control and tumor-bearing animals

• This model can help to investigate the factors responsible for the systemic disposition of ADCs in preclinical animals and human.

• This model could also be applied to assess the risk factors during the drug development

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Acknowledgements Simulation Technologies Team: Viera Lukacova (Team Leader)

Jessica Spires

Jim Mullin

Haiying Zhou

Azar Shahraz

With support from: Michael Bolger (Chief Scientist)

and other teams at Simulations Plus: Computational Technologies

Consulting Studies

ADMET Cheminformatics

Discovery Cheminformatics

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