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The SmartTrack™ Solution

The Challenges in Inhaled Generic Drug Development

Nanopharm and FLUIDDA’s SmartTrack™ can accelerate and derisk inhaled drug product development.

Challenges in Inhaled Generic Drug Development

A standard bioequivalence program for generic inhaled drug products requires systemic pharmacokinetic (PK) data to evaluate bioequivalence (BE). However, as inhaled drugs typically act locally at the point of deposition, systemic PK levels may not be representative of the local rate and extent of drug exposure at the site of action. This is why comparative clinical endpoint (CCEP) studies were historically required. However, it is particularly challenging to show bioequivalence for inhaled generic drug products in comparison to their reference products in CCEP studies because of the inherent variability contributed by patients and the delivery route.

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Although recent U.S. FDA Product-Specific Guidances (PSG) have now provided an alternative way to eliminate or reduce reliance on CCEP studies, the introduction of additional “realistic” in vitro tests in lieu of these has added a different type of complexity. It is common to find that not all in vitro test configurations “pass” the somewhat arbitrary limits of bioequivalence in the lab.

The SmartTrack™ Solution

The collaboration between Nanopharm and FLUIDDA produced SmartTrack™ which is specifically designed for inhaled drug product development. It is a holistic in vitro in silico platform used to put in vitro data into perspective as to whether or not it actually impacts regional deposition in the lungs, and whether that subsequently impacts local bioavailability and thus efficacy. Ultimately, SmartTrack™ helps to interpret whether these observed differences are clinically significant, or whether it can be scientifically justified to widen the bounds to be more biorelevant.

Why is Orally Inhaled Drug Bioequivalence Difficult?

Orally Inhaled drug products are often administered using complex inhaled drug delivery technologies such as pressurized metered-dose inhalers (pMDIs), dry-powder inhalers (DPIs) and soft mist inhalers (SMIs).

The drug delivery pathway is also complex and can be impacted by patient differences, like lung condition, disease, age, inhalation capacity and coordination with the device.

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Regional Lung Deposition Matters

in vitro testing can give an indication of drug exposure, but may not necessarily consider patient to patient - or even intra-patient – variability. Therefore in vitro testing is not a true surrogate for regional lung exposure. in vitro testing does not adequately capture the complexity of the respiratory system, comprised of different regions including the upper, central and peripheral airways.

Additionally, in vitro testing does not account for variability from a patient’s physiology or the human factors introduced when using the delivery device which can impact drug deposition.

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Challenges with the New in vitro Methods

The new realistic aerodynamic particle size distribution (rAPSD) methods that have been introduced in the new PSGs are intended to be a surrogate for regional lung exposure. This is accomplished through the utilization of anatomical throat models representing different sizes from within the population, coupled with realistic breathing profiles or flow rates representative of how a patient inhales the drug product.

These new in vitro methods are in a manner replacing a CCEP of thousands of study participants with a handful of patients in the laboratory under semi-controlled conditions

The coordination between actuation and inhalation has a significant impact on regional drug deposition.

  • A fraction of a second difference in timing between these actions can make a measurable impact when you have a changing flow rate combined with a small and complex throat volume.
  • This is difficult to precisely control in the laboratory, meaning observed differences may actually be attributable to method rather than product differences.
  • Without a clear picture of the cause, bioequivalence may be questioned.
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How Modeling Helps

It would be impossible to run the thousands of experiments needed to capture enough variability through empirical data generation to be representative of the population, let alone control every variable in the lab for such complex methods.   Conversely, in silico simulations do enable the precise control of variables so that development scientists can truly understand what the driving factors are behind the data – either on a very granular or a population level.

  • Account for Variability – Variability in vitro may be due to variability in the test method that is not measured nor able to be controlled. Modeling can assess the sensitivity of these variables and quantify the impact, to justify slight variances from the “acceptable” limits. These in vitro variances are effectively reflecting intra-patient variability (same throat, same breathing profile, different handling).
  • Digital Twins - On a population level, patient behavior and human factors coupled with physiological variability are significant to the outcome of a CCEP study, but cannot be replicated in the lab. Modeling can conduct a virtual clinical trial by creating digital twins, directly utilizing pivotal in vitro data to predict the variation in regional exposure over hundreds or even thousands of patients – in a fraction of the time.
  • Supports Weight of Evidence - Together, these support the “weight of evidence” approach that the U.S. FDA expects, and they help to provide confidence that these marginal differences may not actually be product-related, or in any case, may be out-weighed by variances between patients in a real life setting.

Regulatory Complexity

  • U.S. FDA expectation – U.S. FDA is providing in vitro in silico modelling approaches as an option in Product-Specific Guidances (PSGs), indicating that this data may become the standard approach in future, or that they expect additional data will be required to support the core requirements.
  • Although not deemed “pivotal”, there is still an expectation that the models are sufficiently validated due to the level of risk associated with the decision consequence of the models, and this includes validation of the computational tool’s robustness and sensitivity – not just demonstrating that it is accurately predictive.
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Nanopharm and FLUIDDA’s SmartTrack™ uses an integrated, model-informed data framework with in-vitro and in-silico model data connecting it to clinical performance, beyond just systemic PK values, and reliably predicting the local lung exposure of the drug.

SmartTrack™ is one of the most advanced bioequivalence platforms available for inhaled drug products, having been developed for over a decade with the participation of industry and major regulators.

The result is that SmartTrack™ can provide drug developers with the in vitro and in silico data that is increasingly becoming expected by regulators as it provides a clearer data-driven understanding of inhaled drug delivery and their bioequivalence to reference products. In addition, this confidence can mean that CCEP studies may be waived entirely, potentially shortening the drug’s time to market significantly.

Contact Nanopharm today and accelerate
your inhaled product development program with SmartTrack™

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