Use of In Silico Models to Support Canine Drug and Drug Product

Understanding

Marilyn N. Martinez Senior Research Scientist

US FDA Office of New Animal Drug Evaluation Rockville, Maryland USA

The opinions and information

in this presentation are those

of the author, and do not

represent the views and/or

policies of the U.S. Food and

Drug Administration.

PBPK Models: A Systems Biology Approach

• Provides a mechanism for combining physiological attributes, drug substance, formulation, and clinical variables into predictions of product performance across a patient population.

• Provides an opportunity to identify the most influential variables in determining the shape of the dose-exposure-response relationship.

• Provides a mechanism for predicting potential consequences through the use of “what if” scenarios (e.g., altered formulations, physiology and disease, drug-drug or drug-food interactions).

Variables that Can Influence Drug Exposure and Response

Based upon Huang and Temple, Clin Pharmcol Ther, 2007

EXTRINSIC FACTORS

Drug-drug interactions

Environment

Dose

Diet

INTRINSIC FACTORS

Age Lactation

Pregnancy Gender

Genetics Breed

Disease

Formulation

Route of Administration

The Role of PBPK in Clinical Pharmacology

• Facilitates recommendations for dosage regimens to support product development.

• Enables veterinary practitioners, regulators, and industry to evaluate the potential impact of intrinsic or extrinsic variables on exposure–response relationships.

• In vivo studies are minimized through the use of a confirmatory approach rather than a traditional naïve approach.

• Particularly for companion animal species, there may be times when dose adjustments are needed. PBPK models can help identify potential variables that will trigger specific dose adjustments.

Basic Design of a PBPK Model PBPK models are based upon a separation of “drug data” vs “systems data” This approach allows for an assessment of the differences in PK (PK/PD) in a population before conducting any clinical studies. IVIVE, in vitro–in vivo extrapolation; PBPK, physiologically based pharmacokinetics; PD, pharmacodynamics.

Based upon Rostami-Hodjegan, Clin Pharmacol Therap, 92, 50-61. 2012

TRIAL DESIGN

Physiological Attributes of

Subject Population

Drug Physicochemical

Properties & ADME

Systems data Drug Data

PBPK-IVIVE Linked Models

Simulation output allows for an evaluation of covariates, trial design, formulation, etc. on systemic

drug exposure in the target population

PBPK Models: A Systems Biology Approach

• Unlike compartmental models, the PBPK model can be used to explore “what if” scenarios in terms of intrinsic and extrinsic variables on systemic and tissues exposure.

• A small tissue compartment can be modeled (e.g., CNS) even if changes in its concentration will have negligible impact on blood concentration versus time profiles.

o The tissue exposures are not obscured by the summation of depletion/clearance across the various tissue compartments.

o One can separate organ blood flow, tissue permeability (including transporters), tissue-specific protein binding, local tissue metabolism, etc. to explore how system variables may influence organ-specific responses.

Example: Use of PBPK Models to Explore Organ Toxicity: Liver and Pravastatin.

The purpose of this study was to construct a PBPK model to evaluate the concentration-time profiles for drugs in the plasma and peripheral organs in humans using physiological parameters, scaling factors (scaling factor to translate rat in vitro hepatocyte uptake to in vivo hepatic uptake), and drug-related parameters (unbound fraction and metabolic and membrane transport clearances extrapolated from in vitro experiments).

Watanabe et al., J. Pharm., Exp. Therap, 328:652-662. 2009

Use of PBPK Models to Explore Organ Toxicity: Liver and Pravastatin.

• The key features of this model are as follows: 1) Active uptake (PSinf) blood to liver (OATP1B1); 2) Passive diffusion clearances (PSdif) on the sinusoidal membrane; 3) Active biliary efflux (clearance), liver to bile (PSbile).

• The liver compartment consisted of five units of extracellular and subcellular compartments, connected by blood flow in tandem.

• Question: what might be the effect of increased or decreased efflux or influx transporter activity and how does this relate to observed problems with this compound?

Watanabe et al., 2009

Use of PBPK Models to Explore Organ Toxicity:

Liver and Pravastatin: Sensitivity Analysis.

Based upon Watanabe et al., 2009

Active uptake (PSinf) and passive diffusion clearances (PSdif) on the sinusoidal membrane, and biliary clearance (PSbile) on the canalicular membrane

• Model predictions can be used as “guides” for formulation development, clinical study designs, “red flags” for potential issues of concern, or as a tool to facilitate data interpretation.

• Clearly, the greater the library of systems information, population characterization, and drug ADME characteristics, the better will be our PBPK predictions.

Use of PBPK Models

Kostewicz et al., Eur J Pharm Sci, 57:300-321.2014

Application of PBPK Models in Dogs: Comparison of Ciprofloxacin Absorption

Dogs versus Humans

• Four formulations (IV, oral solution, fast release tablets, slow release tablets) were administered to normal healthy human subjects (16) and mongrel dogs (5).

• Data were modeled in GastroPlus using human or canine modules.

• In vivo dissolution and absorption kinetics were evaluated in both species.

• Location of drug absorption evaluated in both species.

Martinez et al., manuscript in progress

Comparison of Ciprofloxacin Absorption Dogs versus Humans

Based upon the GastroPlus ACAT model, we find that while ciprofloxacin was absorbed throughout the human small intestine, it was primarily limited to the upper small intestine of the dog. This has important drug delivery implications!

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FAST SLOW SOLUTION

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FAST SLOW SOLUTION

Martinez et al., manuscript in progress

Use of PBPK Models to Predict Food Effects in Dogs: Simcyp

• Based upon published data (Cox S et al., (2010) J Vet Pharmacol Therap 33:461 – mavacoxib and Paulson S, et al., (2001) JPET 297:638. Celecoxib), the effect of food on the oral absorption of mavacoxib and celecoxib (poor and extensive canine metabolizers) was modeled in humans and dogs.

• Results were presented as a poster at the American Association of Pharmaceutical Scientists (AAPS) in 2013.

Use of PBPK Models to Predict Food Effects in Dogs vs Humans: Simcyp

To fit the human datasets, the celecoxib solubility in the human GI tract was estimated to be about 10-fold higher than in dogs (Fasted state). As a result, observed and simulated human drug exposure exhibited negligible food effects while, a marked food effect (Fa fed>fasted) was seen in dogs.

Mistry et al., Physiologically Based Pharmacokinetic (PBPK) Models to Predict Food Effects in Dogs, AAPS Poster #W4313, 2013

OBSERVED Vs MODELED RESULTS

Exploring Unexpected In Vivo Results: Danazol: Low solubility high permeability drug

• For a low solubility, high permeability compound (without first pass drug loss or transporter-associated absorption), we would anticipate that bioavailability would improve as particle size and/or administered dose decreases.

• An interstudy comparison of canine oral danazol absorption gave some unexpected results.

• Could in silico (PBPK) models help explore potential reasons for these unexpected study results?

• A sensitivity analysis was generated to help identify potential sources of error -a topic of future investigations/explorations? (Simcyp-CVM collaboration is a work in progress).

See poster on this research by Pade et al.

Exploring Unexpected In Vivo Results: Danazol: Low solubility high permeability drug • The first step was to fit the canine oral bioavailability data

reported by Liversidge and Cuddy (1996).

1. Model IV data (hydroxypropyl-fl-cyclodextrin (HPB)/danazol)

2. Model a 20 mg HPB oral solution (20 mg/kg to fasted dogs). Based upon drug physicochemical characteristics and IV physiological parameter estimates, an excellent fit to the mean reported data was obtained.

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Understanding Prediction Errors • We ran a parameter estimation for the oral suspension and

determined that the best fit to the Liversidge and Cuddy oral suspension data was obtained with intrinsic solubility (IS) = 0.00084 with bile salt on. Fa = 7%.

• Based upon the success of our model in fitting the Liversidge and Cuddy dataset, it was now tested for its ability to successfully model the data generated by Takano et al (Pharmaceutical Research, 25 (2008), 2334-2344).

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Understanding Prediction Errors Unlike Liversidge and Cuddy, Takamo et al., (2008) administered lower danazol doses to dogs. They administered a 2 mg/kg dose as either an “oral solution” (10% dimethyl sulfoxide (DMSO) and 20% vitamin ETPGS as solubilizers) or as drug in capsule (particle size similar to that of Liversidge and Cuddy). Observed Fa = 13.8%

Simcyp simulation of 50 dogs receiving a 2mg/kg oral solution. One lower end prediction matched the mean values reported by Takano et al, 2008. But model still is considered to overpredict danazol exposure 0.00

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Bottom 5% of population prediction

A predicted dog

Observed mean Takano profile

Understanding Prediction Errors Although our model did tend to over-predict the observed profiles associated with a 2 mg/kg oral solution (5µM particle size), we did not expect the magnitude over-estimation we observed when modeling the 2 mg/kg dose of the oral drug in capsules. Our Simcyp model predicted Fa = 45% while the observed Fa was 11%.

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Mean Values of systemic concentration in plasma of Danazol Dog over time - 2 mg/kg

At a low dose, our model results in particle size having an effect only at solubility of about 0.0001. Primary determinant of Fa = IS at high dose but loses its impact at a very low dose. But this is not what was seen in vivo.

Understanding Prediction Errors IS = 0.00084

Understanding Prediction Errors We observed that if we modeled the Vitamin E + DMSO solution as a suspension (with 100% danazol dissolved), the potential for supersaturation and precipitation in the lumen can now be considered. This resulted in a substantial difference in predicted concentrations.

Using a 100% dissolved suspension as the dosage form makes sense as Takano used Vit E and DMSO 10% as solubilizers. Results strongly suggest that intestinal precipitation had occurred.

Understanding Prediction Errors • Another possibility is the potential for the Takano solvent system to

lead to a lower-than predicted absorption of danazol:

• Trapping of drug by vitamin E-stimulated formation of intestinal chylomicrons (Fan et al., Int J Pharm, 445 (2013) 141-147).

• But 10% DMSO appears to decrease movement of substances (e.g., water, and glucose) due to altered basolateral enterocyte membrane.

• Still does not explain capsule

(Mirkovitch and Winistfrfer, Res Exp Me, 187 (1987) 295-302

DMSO-induced decrease in flux: canine isolated perfused

ileal loop

Krebs Buffer DMSO Effect of DMSO

Water (mL/g) 3.9±0.27 0.7±0.14 -

Sodium (mcEq/g) 572±40.1 275±27.5 -

Potassium (mcEq/g 20.2±1.67 10.2±1.28 -

Chloride (mcEq/g) 534±35.4 284±33.6 -Glucose (mcM/g) 56.6±2.06 28.7±1.29

Phenylalanine distribuiton ratio 16.3±1.21 14.2±0.87 NC

B-methylglucoside distribution ratio 11.2±0.73 10.5±0.91 NC

Understanding Prediction Errors A work in progress

• The in vivo data do not correspond with in silico predictions. The sensitivity analysis helped us understand expectations in a linear system. The question is why the predictions ≠observations?

• Since the low dose solution did not have this same magnitude of prediction error, we can effectively exclude saturation of efflux transporters or presystemic metabolism.

• Could there be a dose-associated change in bile salt effect?

• Could solution prediction error be a by-product of the solubilizing agents used? E.G., in addition to challenges with vitamin E and DMSO, danazol was shown to strongly bind to Soluplus (polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol) in equilibrium dialysis experiments, indicating that increased solubility in the presence of Soluplus is compensated by high affinity complex formation to the non-permeable excipient (Linn et al., Eur J Pharm Sci

45 (2012) 336–343).

Summary: Value of PBPK Models in Dogs • Helps to identify interspecies differences that can assist us

when trying to extrapolate human PK date to expectations in the dog.

• It helps in the identification of assumptions that need to be challenged.

• Under a given set of assumptions, it identifies those variables that are not likely to influence drug absorption and tissue distribution.

• It can be used to predict breed differences by altering system parameters. So far, sources of population variability has been explored primarily in humans. However, we are in the process of generating the necessary information to support the incorporation of breed differences in the dog model.

With limitations and cost-constraints facing the animal health community, the use and development of these

models becomes an important mechanism for obtaining information that can impact conditions of use or

formulation development. Through an identification of critical questions, we can limit if and when

additional studies may be warranted.

Thank You for Your Attention