Predictive modeling of deposition, dissolution, absorption and systemic exposure

Transcription

1 Predictive modeling of deposition, dissolution, absorption and systemic exposure IPAC-RS/UF Orlando Inhalation Conference March 20, 2014 Per Bäckman and Bo Olsson, AstraZeneca R&D, Mölndal Sweden The views and opinions expressed herein are those of the presenter and are not intended to represent or reflect the views of AstraZeneca

2 Outline Predicting systemic exposure in early development Dose based approaches Mechanistic models Examples of application and predictive capability Local exposure what conclusions can be drawn 2 Author 00 Month Year Set area descriptor Sub level 1

3 Early Clinical Development Why and when do we need to predict exposure? SAD/MAD POP/Ph2a POC/Ph2b Several potential device/formulation changes driven by: - Flexibility of dosing/time of development - Ability to use at home - Equivalence to commercial product Predicting exposure well will ensure safety and limit changes to local exposure 3

4 Predictive models Based on dose: - Lung deposited dose cast models - Peripheral lung dose deposition models Mechanistic physiology based models 4

5 Predictions based on dose Several possible measures used in parallel for different purposes: most commonly for quality control (e.g. DDU) and as predictor of clinical performance in development Metered Dose Delivered Dose Lung Deposited Dose Retention in inhaler Deposition in mouth and throat Central vs Peripheral Lung Dose Which dose measure is most relevant to use? Depends on purpose and compound properties Modified from Bäckman et al CPT Author 00 Month Year Set area descriptor Sub level 1

6 Lung Deposited Dose Olsson B, et al : Validation of a general in vitro approach for prediction of total lung deposition in healthy adults for pharmaceutical inhalation products, JAMP Epub. Available online Feb 19, 2013, doi: /jamp Excellent correlation between systemic exposure in vivo and ex-opc cast dose for compounds with 100% lung bioavailability

7 Deposition Pattern Predicting the deposition pattern of an aerosol based on a model of deposition efficiency as a function of inhalation maneuver and particle size distribution (ex-cast impactor data) ICRP, Human Respiratory Tract Model for Radiological Protection. ICRP Publication 66. Ann. ICRP 24 (1-3) Deposition Efficiency Peripheral Central Central Lung Dose Peripheral Aerodynamic Particle Size (µm) Size (µm) Flow (l/min) Central to peripheral dose ratio (C/P) is expected to vary with device and formulation 7

8 Mechanistic physiology based models 1,2 Linking differential equations describing key events to enable prediction of local and systemic exposure Aerosol deposition pattern (MMAD, air flow); dissolution (MMD, Solubility); Mucociliary transport Permeation, tissue binding (log P, pk a, R bp, F up ), diffusion (blood flow) Distribution volume, Clearance (PK-model from iv) 8 Author 00 Month Year Set area descriptor Sub level 1 1 Modified from : Olsson and Bäckman, Respiratory Drug delivery 2014; 2 See e.g. the mechanistic physiologically based pulmonary compartmental absorption and transit model (PCAT ) (Gastroplus, Simulations Plus, Lancaster, CA, USA).

9 A case study Predicting systemic exposure of an inhaled selective glucocorticoid receptor modulator (SGRM) with low water solubility FF 2 FP 1,2 Likely to show a dissolution rate limited systemic absorption? SGRM 3 BUD 1 1 Thorsson et al: Pharmacokinetics and systemic acticivity of fluticasone via Diskus and pmdi, and of budesonide via Turbuhaler J Clin Pharmacol. (2001) 52: ; 2 Allen et al: fluticasone Furoate, a novel inhaled corticosteroid, 9 Author 00 Month Year Set area descriptor Sub level 1 demonstrates prolonged lung absorption kinetics in man compared with inhaled fluticasone proprionate Clin Pharmacokinet (2013) 52:37-42; 3 Unpublished data, Prothon et al

10 Particle size Observed plasma profiles after dosing an inhaled SGRM with low water solubility at different geometric particle sizes (1.3 vs 3.1 µm). Same device/formulation (nebulizer suspension) and lung dose (±5%), similar deposition pattern (12% difference in peripheral dose caused by a slight difference in droplet size (5.9 vs 7.7 µm) 1.3 µm Likely a result of differences in dissolution rate Figure 2: Systemic exposure after dosing a poorly soluble compound at two different geometric particle sizes: 1.3 um (open) and 3.1 um (solid). Modified from : Olsson and Bäckman, Respiratory Drug delivery Author 00 Month Year Set area descriptor Sub level 1

11 Predicting systemic exposure lung dose Comparing systemic exposure (AUC) of an inhaled selective glucocorticoid receptor modulator (SGRM) with low water solubility when dosed to healthy volunteers using different devices and formulations used or intended to be used in the clinical development 12 Observed AUC (nmh) R² = Lack of correlation between observed exposure (AUC) in man and predicted lung dose (OPC Cast dose) How can we rationalize and predict exposure in coming studies? Ex-Cast Dose (µg) Modified 11 Author from : 00 Olsson Month and Year Bäckman, Respiratory Drug delivery 2014 Set area descriptor Sub level 1

12 Predicting systemic exposure peripheral lung dose Comparing systemic exposure (AUC) of an inhaled selective glucocorticoid receptor modulator (SGRM) with low water solubility when dosed to healthy volunteers using different devices and formulations used or intended to be used in the clinical development Observed AUC (nmh) R² = AUC (nmh R² = Ex-Cast Dose (µg) Peripheral Dose (µg) Systemic exposure (AUC) significantly better correlated to peripheral lung dose (generations 9-23) than to total lung deposited dose. Modified 12 Author from : 00 Olsson Month and Year Bäckman, Respiratory Drug delivery 2014 Set area descriptor Sub level 1

13 Summing up Predicting systemic exposure based on dose Total lung dose only predictive for exposure of compounds with complete lung bioavailability (and no oral bioavailability) Peripheral lung dose may be a more useful predictor of exposure for compounds with low water solubility (<1µM?) (and no oral BA) 13

14 Mechanistic physiology based models 1,2 Linking differential equations describing key events to enable prediction of local and systemic exposure Aerosol deposition pattern (MMAD, air flow); dissolution (MMD, Solubility); Mucociliary transport Permeation, tissue binding (log P, pk a, R bp, F up ), diffusion (blood flow) Distribution volume, Clearance (PK-model from iv) 14 Author 00 Month Year Set area descriptor Sub level 1 1 Modified from : Olsson and Bäckman, Respiratory Drug delivery 2014; 2 See e.g. the mechanistic physiologically based pulmonary compartmental absorption and transit model (PCAT ) (Gastroplus, Simulations Plus, Lancaster, CA, USA).

15 Predicted Systemic Exposure Mechanistic Modeling 1,2 A mechanistic model 2 of deposition, dissolution; mucociliary clearance, absorption and diffusion through lung tissue was used to predict systemic exposure (AUC) Observed AUC (nmh) R² = Observed AUC (nmh) R² = Observed AUC (nmh R² = Ex-Cast Dose (µg) Peripheral Dose (µg) Predicted AUC (nmh) Mechanistic models, when accurately informed by deposition pattern, phys-chem data and a PK-model, have potential to predict systemic exposure of neutral inhaled drugs with low water solubility 15 Author 00 Month Year Set area descriptor Sub level 1 1 Modified from : Olsson and Bäckman, Respiratory Drug delivery 2014; 2 Mechanistic physiologically based pulmonary compartmental absorption and transit model (PCAT ) (Gastroplus, Simulations Plus, Lancaster, CA, USA)

16 Modeling systemic exposure 1 Actual 2 (squares) and predicted 2 (solid line) plasma profiles following dosing of a poorly soluble SGRM via: iv, p.o., and by inhalation to HV (n=18) using 4 different inhalers. IV Nebulizer 1 DPI 1 PO Nebulizer 2 DPI 2 Combining an experimentally derived iv model to account for systemic distribution and clearance with phys chem data (solubility, lipophilicity, permeability, primary particle size, f up, R bp ) to account for dissolution, permeation and diffusion in to the system, and a predicted deposition pattern based on impactor data allowed for reasonable predictions of actual Author 00 Month Year Set area descriptor Sub level 1 exposure 16 in HV. 1 Unpublished data, Bäckman & Olsson, 2 Mechanistic physiologically based pulmonary compartmental absorption and transit model (PCAT ) (Gastroplus, Simulations Plus, Lancaster, CA, USA).

17 What conclusions can be drawn wr to lung tissue exposure? Predictions of dissolution profile, concentration in airway lumen, concentration in airway tissue and extent of absorption derived from model used to predict plasma profiles Solid drug [Lumen] [Tissue] Absorption Peripheral Central Useful information to drive development of new compounds Predicted tissue levels not verifiable in man? Total lung amount only an indicator of solid drug Systemic exposure only an indicator peripheral absorption 17

18 Predicted disconnect between systemic and local exposure Plasma profiles and local tissue concentration profiles were generated for two cases: a 1:1 Central: Peripheral deposition profile (red) and a 5:1 C:P deposition profile (blue). Peripheral dose was kept constant. Solubility < 1 µm Tissue concentration/ pg/ml fold A Plasma concentration/ pg/ml Overlapping profiles B Time/h Time/h Predictions indicate that local tissue exposure (A) is sensitive to variations in centrally deposited dose; whereas systemic exposure (B) is insensitive 18 Author 00 Month Year Set area descriptor Sub level 1 Unpublished data, Bäckman & Olsson

19 Summing up Predicting exposure based on mechanistic models Mechanistic models have a use in development of a new medicine, for example: As a complement to support predictions of systemic exposure when changing device/formulation in early clinical studies and in selecting the optimal dosage form for commercial development As a tool to support optimization of duration and efficacy of new inhaled drugs However, the use of mechanistic models as surrogates for pivotal clinical studies is still in the future as the predictive capability wr to local exposure in man is not known/testable 19 Author 00 Month Year Set area descriptor Sub level 1

20 What remains to be done? A wish-list Disease state physiological models Validation of tissue levels A validated in vivo relevant dissolution model Improved permeability models ( P app -P eff ) Models accounting for biotransformation and entrapment 20

21 Acknowledgements Lars Borgström Holger Adelmann Adriaan Cleton Anders Broo Helen Jackson Francois Guillou Ulf Eriksson Antonio Llinas Markus Fridén Ulrika Tehler Johan Solandt Peter Aldred Jan Westergren Mikael Brülls Sheila Peters Susanne Prothon Walter Lindberg Kerstin Strandgården Karin Carlsson Karin Jorup Mark Hindle Thomas Lööf Elisabeth Lindqvist 21 Author 00 Month Year Set area descriptor Sub level 1