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Targeted Drug Delivery to the Lung University of Sheffield EC funded COPHIT project CFX Ansys...... Areco..................... Aventis....……………. INO Therapeutics....

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Presentation on theme: "Targeted Drug Delivery to the Lung University of Sheffield EC funded COPHIT project CFX Ansys...... Areco..................... Aventis....……………. INO Therapeutics...."— Presentation transcript:

1 Targeted Drug Delivery to the Lung University of Sheffield EC funded COPHIT project CFX Ansys Areco Aventis....……………. INO Therapeutics.... University of Mainz.. UK France UK Austria Germany Partners

2 Outline  What is targetted delivery? –– the COPHIT project  Systemic model for whole respiratory system  Validation –Inhaled Nitric Oxide –Inhaled hyper-polarised 3 He –Particles inhaled via dry powder inhaler

3 Inhaled drug delivery - I  Obvious: lung diseases –Asthma – COPD – Cystic Fibrosis – Pulmonary Hypertension

4 Inhaled drug delivery - II  New not-so-obvious therapies: –Diabetes - Insulin –Pain Management - Morphine –Multiple Sclerosis - Interferon Beta 1a –Osteoporosis - Parathyroid hormone –Infectious Disease - Antibiotics  Lots of advantages but...

5 The metered dose inhaler  The drug is dissolved in the propellent  The actuation causes the drug to leave the device at 25 ms -1

6 Sub-optimal delivery...

7 …sub-optimal results  Most of the drug impacts in the mouth  The small amount in the lungs is distributed only in the proximal airways

8 Controlled Entrainment

9 Targeted Drug Delivery Controlled entrainment:  Droplet/particle size  Delivery timing  Delivery duration  …profiled to match the patient’s characteristics And is leading to a new generation of devices  Processor-controlled  Adaptive

10 Multi-variant Optimisation Hundreds of variables…  Device characteristics  Drug formulations  Breathing patterns  … How to optimise?  Only by modelling…

11 Ideal Model Tell the model about…  Device behaviour  Drug characteristics  Patient’s geometry, pathology, breathing It reports back on…  Deposition  Uptake  Effectiveness

12 COPHIT: Project AIM To develop a comprehensive dynamic compartmental model that can track the progress of inhaled drug delivered from the device through the respiratory system and into the circulation…. …validated in man by MR-imaging with hyper-polarised 3-He, and other techniques Computer-Optimised Pulmonary Delivery in Humans of Inhaled Therapies

13 Complex Physiological System The airways - branching into several thousand pathways through more than 20 bifurcations  The lung million alveoli where drugs can be taken up across the alveolar membrane

14  3D Device from CAD Model Compartments  3D airways to G8 from scans  0/1D compartments thereafter  Variable pathologies G8: 2 8 =256  3D URT geometry from scans

15 3D geometries from medical images Fill in missing detail Segmentation of high resolution CT scans

16 Geometry reassembled

17 Automatic surface mesh created

18 Upper Respiratory Tract

19 Tracheo-bronchial tree…

20 …down to the 8th generation 1.34 million volume elements (tets) Time ~4mins on a 800MHz P3 requiring ~1Gb Ram WP3

21 Diseased airways

22 Flow and Deposition Analysis  Full Navier-Stokes equations solved in 3D geometry  Subject to certain boundary conditions  Commercially-available CFD software package from partner –CFX 5.6 from CFX Ansys –Extra facilities for aerosol modelling

23 Transient flow: peak Re = 1200 t V

24 t V

25 t V

26

27

28

29 Massless particles in transient flow

30 t= s Upper respiratory tract: transient

31 t=0.8-1s WP3 Upper respiratory tract: transient

32 Validation

33 Validation – 5 Strategies  5 approaches to validation… –Gas – Sampled (INO Therapeutics) –Gas – MR Imaged (Mainz & Sheffield) –Aerosols, Medical – Imaged(Aventis) –Aerosols, Industrial – Analysed(Areco) –Powders – Scintigraphically Imaged(Aventis)

34 Particles – Scintigraphy images  3 different inhaled regimens (A,B and C)  Scintigraphy images of inhaled radiolabelled dry powder and aerosol  Deposition calculated (from images) as % of initial dose in –lung –oropharynx –oesophagus and stomach –exhalation filter –device

35 Particles – PK measurements  Blood plasma concentration measured.

36  -Scintigraphy - Results –Regimen A: Eclipse™ at optimal inspiratory flow rate 50 L min -1 Eclipse™ optimal flow rate – 30% retention 24% initial dose26% initial dose

37  -Scintigraphy - Results –Regimen B: Eclipse™ at sub-optimal inspiratory flow rate 30 L min -1 Eclipse™ sub-Optimal flow rate – 50% retention 12% initial dose 26% initial dose

38  -Scintigraphy - Results –Regimen C: pMDI at optimal inspiratory flow rate 40 L min -1 pMDI Optimal flow rate – 9% retention 4% initial dose 7% initial dose

39 Particles Simulations – Device

40 Particles Simulations – Mouth 0.5  m 3 m3 m 7 m7 m 20  m 50  m

41 Particles Simulations – Mouth 0.5  m 3 m3 m 7 m7 m 20  m 50  m

42 Particles3

43

44 Dynamic deposition

45 Quantification of deposition data - I

46 Quantification of deposition data - II

47 Quantification of deposition data - III 44% 28% 3%

48 Gas Sampled  Artificially ventilated patients Experimental trials at INO Therapeutics

49 Resulting mass fractions of NO - III Coupled

50 Resulting mass fractions of NO - III Coupled

51 Resulting mass fractions of NO - IV Coupled

52 Comparison with trial results – I Five points of measurement are identified Trachea Right main bronchus Left main bronchus Right down lobe Left down lobe

53 Comparison with trial results – II

54

55 Gas – MR imaged Experimental trials at Sheffield

56 Gas – MR imaged  maximum temporal resolution =5.4 ms  1L of 3He and Nitrogen breathed spontaneously from a bag  gas composition = 300cm 3 3 He, 700cm 3 N 2 Experimental trials at Sheffield

57 Simulation results

58 Comparison with dynamic MRI - I

59 Comparison with dynamic MRI - II

60 Comparison with dynamic MRI - III

61 Software - processes

62

63 Easier to use front-end…  Uses web driven software – EASA from AEA Technology  All licensed software (e.g CFX etc) sits on a remote EASA server  Application driven from client’s computer by web browser and EASA client software

64 Conclusions  Comprehensive simulation tool developed  Allow pharma companies to test devices virtually before human trial  Enable clinicians to investigate specific pathological scenarios  Limitations: –Validation –Models


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