DissoAmerica 2022 - Session 3 - Tessa Carducci

Predictive Dissolution Modeling –Driving Towards Real Time Release Testing

Presenter: Tessa M. Carducci, PhD (she/her)

Disso America 2022

Overview of talk

Role of modeling in mechanistic understanding and identifying factors that impact dissolution

Intro to in vitro predictive dissolution modeling (PDM) for drug product release

Development of a PDM for release of a drug product

Role of modeling in mechanistic understanding and identifying factors that impact dissolution

Types of dissolution modeling in the realm of predictive technology

BCS II/IV likely to be solubility rate limited

First

modeling approach

Nernst-Brunner

Case example using first principles modeling to test dissolution mechanism understanding

• Parameters that are likely to impact dissolution: API particle size and media pH

• Modeled according to first principle models (NernstBrunner, DDD+ Software)

• Simulated results compared to dissolution experiments of tablet DoE made with 3 different API PSD in different pH media

• Ensured the DoE would be foundational to future multivariate modeling efforts by including additional measurements, e.g. NIR, hardness, and disintegration

Nernst Brunner Modelusing DDD+

Early stage multivariate modeling

Regression using Weibull Model

of

on

Learnings for dissolution mechanistic understanding

Need multivariate model that uses training set

• Although tablet is BCS IV the dissolution is rapid across pH exhibiting “pseudo-BCS III” behavior

• First principles model used equilibrium solubility measurement so failed to predict the rapid dissolution in pH 6.8

• Addition of apparent solubility measurement into the model increases prediction accuracy but does not incorporate CMAs/CPPs

• Rate limiting step in this case is disintegration meaning that compression force and other scale effects are likely to play a significant role in the prediction of

dissolution response

Fishbone diagram for dissolution performance understanding

Blending Compression

Blend uniformity

Extent of blending

Tablet weight Blending sensitivity

Compression force

Tablet hardness

Blend physical properties

Tablet relaxation

API concentration Dissolution

Tablet temperature

API physical attributes

Water activity

Excipient physical attributes

Weight gain

Raw Materials

Film Coating

Intro to in vitro predictive dissolution modeling (PDM) for drug product release

What is in vitro predictive dissolution modeling (PDM) for release?

2012 Q8, Q9, and Q10 Q&A FDA guidance for industry

•“…a multivariate model (e.g. a partial least squares (PLS) model), when appropriate, can be used as a surrogatefor traditional dissolution testing. In this case, the PLS model should be developed in terms of inprocess parameters and material attributes and can be used to predict dissolution.” -

EMA’s 2012 “Guideline on Real Time Release Testing”

•“Attributes relating to the properties of a tablet granule such as porosity, particle size, surface area, bulk/tapped density etc. if shown to have a predictive relationship with dissolution behaviour could serve as RTR testing surrogates for dissolution testing. These dependencies would have to be confirmed on a productby-product basis.”

•“Predictive dissolution modeling is an emerging methodology defined as the ability to mathematically generate a time profile of the dissolved amount of an API based on information regarding material properties, dissolution method conditions, formulation composition, and process parameters.”

IQ Workshop Summary Report, 2022

•First-principles and empirical approaches to predicting in vitro dissolution for product release testing…predicting a quantitative value of the level of drug released at a specific time point, as is done traditionally with a physical dissolution test.

AAPS Whitepaper 2019

Can we implement RTRT for any/every product?

Immediate release

BCS class I or III

Very stable (no degs)

Drug loading Dev. timeline constraints

Global regulatory acceptance

Real time release testing scheme for non-BCS I/III drug product

(current)

(future)

peak volume, RTRT has 34% FTE savings benefit incremental to Rapid Release

Batch

-Large, centralized facilities

-Few campaigns in large batches

-Large inventories

Continuous

-Agile facilities

enables flexibility

-Many campaigns with quick turnaround -Smaller inventory

To realize full benefits of continuous manufacturing, RTRT is imperative

RTRT is required to keep up with batch release!

for release yields strategic and business benefits

business drivers

RTRT to low solubility

Overview of developing a PDM for release

Early development

Latedevelopment

Validation and implementation

mechanistic understanding of dissolution response

dissolution sensitivities through small scale

QC method selection justification is strong

materials risk assessment

through

dissolution

dissolution method robustness and minimize unwanted variability

regression between

response

the

maintenance

Development of a PDM for release of a drug product

Dissolution performance

specification strategy

•

dissolution method: 100 rpm baskets, pH 6.8

Q = 80% in 20 minutes

Time(min)

model strategy

BCS IV but relatively fast dissolution due to high apparent solubility of the API across pH

• Supersaturation in pH 6.8 disso media stabilized by HPMC in the formulation

• Dissolution is sensitive to tablet hardness, and %RH at early time points

Method is discriminating for API form as exhibited by lower plateau region in presence of freebase

Initial lag time due to film coat removal but has little/no impact at Q time point

Early time points have high variability

strategy for

Fishbone diagram for dissolution performance understanding

Blending Compression

Blend uniformity

Extent of blending

Tablet weight Blending sensitivity

Compression force

Tablet hardness

Blend physical properties

Tablet relaxation

API concentration Dissolution

Tablet temperature

API physical attributes

Water activity

Excipient physical attributes

Weight gain

Raw Materials

Film Coating

PDM DoE at commercial scale, sampling strategy, and data aggregation

• Parameters: API size, blending and lubrication, film coating conditions, compression conditions, and film coating weight gain

• Scanned tablets by NIR for %LC and spectral information before dissolution

• Measured tablet weight, thickness, and diameter before dissolution

• Utilized Spotfire platform to combine dissolution and process data together

Y-block, and model selection strategy

Variance in the dissolution data is systematic and/or random

Early time points in profile are highly variable, expected to be more sensitive to systematic process and environmental conditions

R2 is a measure of the percentage of variance captured by the model

Later time points have less variance (both systematic and random)

Exploratory analysis: regression at 5 min dissolution timepoint

Loading plot (shows directionality of impact)

% dissolved (measured) vs % dissolved (predicted)

Predicted vs. measured shows high (~90%) of variance explained by model

Exploratory analysis: regression at 10-and 15-minute timepoints

% dissolved (measured) vs % dissolved (predicted) @ 10 min

% dissolved (measured) vs % dissolved (predicted) @ 15 min

Predicted vs. measured at later time points shows high accuracy but lower % of variance explained (as expected)

Preliminary model X-block

Core tablet weight

Correlates with API conc. for DC process

Encompasses impact of moisture

comp force

Encompasses impact of API PSD, fill depth, etc.

Model the full dissolution profile via fit coefficients

Low resolution

Whitney

High resolution

Gompertzequation

Model options analysis based on technical performance

Rank of FIM ⇒ number of parameters that can be uniquely estimated (measure of correlation)

Condition Number of FIM ⇒ indirect measure of accuracy, measures if the matrix is ill-conditioned

ModelLow resolutionHigh resolution

MNWVery high condition number

Inconsistent rank across the design space

Weibull Very low condition number

Consistent rank (= the number of total parameters)

Gompertz Very low condition number Consistent rank (= the number of total parameters)

Relatively higher condition number (higher covariance expected) Consistent rank (= the number of total parameters)

Very low condition number Consistent rank (= the number of total parameters)

Very low condition number Consistent rank (= the number of total parameters)

•

• Low resolution data preferred for ease of model maintenance in supply

• Not possible to estimate all the parameters in MNW model using low-res data

Gompertz model explained the disso profile better especially at approach of the plateau region

Step 1 Model: Demonstration of model via fit equations

Step 1 –Apply the fit equation model through a set of lab dissolution (low res) profiles

Comparing the Gompertz fit vs actual % dissolved Gompertz equation

R2 = 0.99

Regressing disso curve parameters against process conditions

DissolutionStep2Model_May2022.M8 (PLS), Gompertz k1 - old

according to Obs ID (Batch Identifier)

block

5.413e-008

0.5166

Compression Force

Depth

K

DissolutionStep2Model_May2022.M9 (PLS), Gompertz k2 -

according to Obs ID (Batch Identifier)

block

1.988e-007

Step 2 Model: Regressing disso curve parameters against process conditions

Parameters:

Weight

Weibull: �� 1 exp

Hardness

Compression Force

Depth

K

DissolutionStep2Model_May2022.M6 (PLS), Weibull k1 - old X block Colored according to Obs ID (Batch Identifier)

= x + 7.765e-008 R2 = 0.4912

= 0.431989 RMSEcv = 0.433865

B

C Batch D Batch E

A

DissolutionStep2Model_May2022.M7 (PLS), Weibull k2 - old X block

Colored according to Obs ID (Batch Identifier)

= x + 5.06e-007

= 0.7305

F

= 1.35908

= 1.36993

A Batch B Batch C Batch D Batch E Batch F

Predicted vs. measured for calibration training set

Red –Measured

Black –Predicted

Average residual is 1% and most predicted dissolution results at spec time point are within 3% of the lab dissolution result

Next steps for model validation and implementation

Refine model development

analysis

quantification

x-block (iterative)

Model operationalization & maintenance

Model validation strategy

•Based on classical validation elements

independent data

there is consensus in field that validation is only needed for Q timepoint

Use

Verification

Can we build a model using scale independent parameters?

Scale –dependent

Main compression force

Hardness

Blending constant

NIR (%LC)

Core tablet weight

Film coat weight gain

Scale –independent

Hardness

Bulk/tapped density

NIR (%LC)

Core tablet weight

Film coat weight gain

Pros: Likely to be lower cost for development and model maintenance, can use small scale equipment so does not need to take up time on manufacturing site equipment, more easily able to do batch perturbations at development site to push towards failure points

Cons: Parameters may not be equivalent, model may not be as robust, and parameters may not always be easier to measure

Alternate modeling approach: spectrum-based (PAT-based) model

NIR classification model has shown distinction between slower dissolving batches

Pros: NIR spectrum is a tablet measurement –no need to regress process parameters, measurement is applied to the final product which gives more confidence in product quality

Cons: Phenomena being measured may not always be understood or may not be meaningful

for a batch versus a continuous process:

Open questions –#1

How does sampling and assessing against the specification work for PDM?

Dissolution models for release previously approved by FDA followed USP <711> using N = 12 tablets and assessing against Stage 2 acceptance criteria.

Following USP <711> will allow for most alignment between lab disso and disso model sampling and result assessment and will also make model maintenance more straightforward

Another possible approach is to use a statistically-based assessment criteria with a larger number of replicates

This approach may make more sense for continuous processes where large(r) N is needed

Additionally, RSD at Q timepoint for this product is low and does not necessitate an increased number of replicates.

Open questions -#2

How to show discriminatory power of the PDM model?

• Dissolution model are expected to be as sensitive as the lab QC dissolution method especially for CMAs/CPPs

• For multivariate models, the model inherits the variance (discrimination power and random variability) of the lab QC dissolution method because the model is trained using real data as input.

• The dissolution mechanism and method selection are important foundations to model development. For a multivariate model, changing the QC dissolution method would render the model training set basically useless.

• Need to ensure that the input data covers the various dissolution dependencies that exist and incorporates many potential sources of variation in the inputs (i.e. raw materials attributes).

• Ability of PDM model to detect specification failure is very important either using a real batch (ideal) or by using a virtual batch as input.

• In routine supply, the model can be updated with more data as more variation of inputs are seen.

Open questions –#3

How are lab dissolution and PDM results handled when generated simultaneously?

There are scenarios where both lab dissolution results and model results will be generated for the same batch i.e. during routine parallel testing for model maintenance or for countries that do not have the disso model filed/approved

Lab dissolution may also be run shortly after the model prediction as post release testing (i.e. TOI or stability T0) by downstream sites.

How will we gauge if model and lab results are aligned? Do we need to?

Define when assessment needs to be performed in the quality system

Use a sound statistical approach (i.e. 95% CI) and/or follow method transfer criteria (i.e. % dissolved at Q is within +/-5%)

Having f2 or other profile similarity criteria is not ideal because a dissolution profile is not routinely measured for release by lab

What happens if the lab disso result does not align with the result predicted by the model? Model maintenance will be triggered and involve updating the model.

Open questions –#4

How close are we to global acceptance of PDM for release?

• There are several successful examples of PDMs for release approved in US, EU, and possibly other markets.

• There was recently a new MFDS (Korean agency) regulation on RTRT for new products that mentions predictive dissolution modeling.

• Our efforts to influence regulatory guidelines and put forth innovative filing applications for new products will pave the way for future programs with RTRT strategies towards global acceptance.

• Can be used in combination with physiologically-based biopharmaceutics modeling (PBBM) to support clinically relevant justifications. Building clinical relevance into RTRT can help strengthen package and boost chances of regulatory acceptance.

Overall summary and conclusions

•

Mechanistic understanding matters for dissolution method selection, justification, and to lay a foundation for future commercial release strategies

• Investment in modeling to understand dissolution mechanism may prove useful in investigations down the line

• Predictive dissolution modeling for release can be a key enabler for RTRT and CM strategies for low solubility drug products

• Although there are already successful approvals for use of predictive dissolution modeling for release, it is still a relatively new regulatory concept that will benefit from pharmaceutical companies pushing the envelope by filing innovative strategy proposals globally for model validation, assessment, etc.

Acknowledgements

Contributors & supporters

Nathan Pixley

Jasdeep Mandur

Andre Hermans

Leah Xiong

Kevin Reuter

Anushka Gupta

Diana Watkins

Chitra Telang-Lobo

Kevin Reuter

Plinio de los Santos

Chris Pridgen

Courtney Stanton

Azzeddine Lekhal

Brandye Smith-Goettler

Plamen Grigorov

Andrew Buss

Shailesh Singh

Mano Ramasamy

Asha Rajapakshe

Krista Witkowski

Thank you!