CFD Models to aid the development of generic inhalation products | FOR FD 20 023

FAQs: FDA CFD Modeling Grant for Generic Orally Inhaled Drug Products (FOR FD 20 023; CFDA 93.103)

1) What is this FDA grant opportunity about?

This opportunity supports research that uses computational fluid dynamics (CFD) modeling to improve the development of generic orally inhaled drug products, specifically metered dose inhalers (MDIs) and dry powder inhalers (DPIs). The work is intended to strengthen the scientific link between common in vitro performance tests and what matters clinically: where, how much, and how efficiently drug is delivered to regions of the lung.

2) Which inhaler types are in scope?

The solicitation focuses on generic orally inhaled drug products, explicitly including MDIs and DPIs.

3) What problem is the program trying to solve?

FDA product-specific guidance for demonstrating bioequivalence for these inhaled products typically relies on a weight-of-evidence package. While workable, this approach can be expensive and challenging for generic developers, in part because it is not always clear how standard in vitro metrics translate to the rate and extent of drug delivered to intended lung regions.

4) What is the typical "weight-of-evidence" bioequivalence package referenced in the opportunity?

The opportunity describes a package that commonly includes: (1) in vitro performance testing, (2) an in vivo pharmacokinetic (PK) study, and (3) either an in vivo pharmacodynamic (PD) study or a comparative clinical endpoint (CCEP) study.

5) What is the primary technical goal expected from awardees?

The main goal is to generate CFD-based predictions of regional lung deposition for at least one currently marketed MDI or DPI, covering both upper and lower human airways. In practical terms, applicants are expected to build models that simulate aerosol transport through realistic airway geometries and predict where particles deposit.

6) Does the project need to focus on one product or multiple products?

The solicitation expects CFD-based predictions for at least one currently marketed MDI or DPI. It does not state that multiple products are required in the provided excerpt.

7) What airway regions must be included in the modeling?

The model predictions are expected to cover both upper and lower human airways.

8) What does FDA emphasize about model credibility?

The announcement emphasizes verification and validation. Models should not only be created, but also checked for numerical correctness (verification) and shown to match reality (validation) using appropriate data sources.

9) What is meant by "verification" in this context?

Verification refers to checking the model for numerical correctness. In other words, it is about ensuring the computational implementation behaves as intended from a numerical standpoint.

10) What is meant by "validation" in this context?

Validation refers to demonstrating that model predictions match real-world behavior. The solicitation indicates validation can be performed using in vitro data (for example, established aerosol characterization or airway replica testing) or in vivo data where available.

11) What data sources can be used to validate the CFD predictions?

The excerpt indicates that validation may use either in vitro data (including established aerosol characterization or airway replica testing) or in vivo data where available.

12) After verification and validation, what additional analysis is expected?

After a model has been verified and validated, the project is expected to run a parameter sensitivity analysis. This is intended to test how changes in key inputs and device/formulation attributes influence predicted regional deposition.

13) Why is parameter sensitivity analysis important in this program?

The intent is to understand which factors most strongly affect predicted regional deposition and to use those insights to evaluate how biopredictive common in vitro tests really are. Over time, this can help clarify which laboratory measurements best predict in vivo delivery.

14) How does the program connect CFD results to in vitro testing?

The program anticipates using sensitivity analysis results to map changes in in vitro metrics to changes in predicted deposition in specific lung regions. This can identify which in vitro measurements track well with regional deposition and which may be insensitive or potentially misleading.

15) Are any specific in vitro metrics mentioned?

Yes. The opportunity mentions aerodynamic particle size distribution (APSD) as an example of an in vitro metric, along with other performance measurements.

16) What regulatory-relevant outcome is FDA ultimately trying to support with these models?

The solicitation indicates that the models should become credible enough to inform regulatory-relevant questions about inhaler performance and equivalence, by producing trustworthy predictions of regional lung deposition.

17) Does the opportunity encourage using DEM along with CFD for DPIs?

Yes. For DPIs, FDA notes that discrete element modeling (DEM) may be incorporated to better represent particle-particle interactions, especially agglomeration and deagglomeration processes in carrier and active pharmaceutical ingredient blends.

18) Why might CFD-DEM be useful for DPI research under this award?

The excerpt explains that DPI performance is heavily influenced by how powders break apart and disperse during inhalation. Coupling DEM with CFD can improve predictions of how micro-scale powder behavior translates into aerosol formation, APSD outcomes, and ultimately regional deposition.

19) Is there any stated preference for MDI formulations?

Yes. For MDIs, the opportunity signals a preference for studying formulations with at least three components, reflecting that many modern MDI systems involve multiple constituents that can affect spray formation, droplet evaporation, plume dynamics, and deposition.

20) What kinds of MDI behaviors or phenomena are considered relevant to deposition modeling?

The provided description notes that formulation constituents can affect spray formation, droplet evaporation, plume dynamics, and deposition, all of which are relevant to predicting regional lung deposition.

21) Who administers this grant program?

The program is run by the Department of Health and Human Services (HHS), Food and Drug Administration (FDA), and is offered as a discretionary grant.

22) What is the Funding Opportunity Number and CFDA listing?

The Funding Opportunity Number is FOR FD 20 023, and the CFDA number listed is 93.103.

23) Who is eligible to apply based on the information provided?

Eligibility is described as broad and includes: state and local governments; tribal governments and organizations; public and private institutions of higher education; nonprofits (with or without 501(c)(3) status); for-profit organizations (with an exclusion noted for small businesses in one category while separately including small businesses as eligible); and certain housing authorities.

24) How many awards does FDA expect to make?

The opportunity posting indicates an expected number of awards of two.

25) Is there a stated maximum award amount (ceiling)?

The posting lists an award ceiling of 0. The excerpt notes that this typically means the ceiling was not specified in the excerpted listing, rather than indicating no funding.

26) What is the posting date shown for this opportunity?

The posting date is May 25, 2021.

27) Is the closing date clearly provided?

The original closing date is shown as an archiving forecast, suggesting the listing may have been moved into an archived status or that the date was not populated in the extracted record.

28) What longer-term impact does FDA suggest this research could have?

The description indicates the work could reduce uncertainty for generic developers by clarifying which in vitro measurements are most predictive of regional deposition, better aligning testing strategies with clinical relevance, and supporting more efficient, evidence-based bioequivalence approaches for MDIs and DPIs.