# (140e) Optimizing Drying Profile of Polymeric Drug Products Using Machine Learning and First Principle Modeling

- Conference: AIChE Annual Meeting
- Year: 2018
- Proceeding: 2018 AIChE Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
- Session:
- Time:
Tuesday, October 30, 2018 - 2:10pm-2:35pm

Drying is one of the critical

steps for manufacturing of polymeric drug products. The key quality attributes

for the dried product can be the residual solvent content, and the flowability.

The flowability of the powders can be dependent on the glass transition

temperature which in turn depends on the solvent content. Hence it requires

special precautions to keep the temperature of the drug product during drying below

a critical transition temperature (Tg). This work shows a case study where such

type of issues can be tackled by employing a hybrid model combining machine

learning and first principles to optimize the drying profile in minimal number

of experimental runs.

Typically the drug product is

dried in an agitated Nutsche filter type of a dryer. This drying requires

special care to ensure that at all points of drying; the temperature of the product

is below the critical value (Tg ). While a simple heat transfer model (like in

Figure 1, section A) can be used to link the jacket temperature to the product

temperature, the presence of solvent and its evaporation needs additional

considerations as detailed below. In the model building step, Model 1 was a

Support Vector Machine (SVM) based model to predict the Tg for different

residual solvent levels (based on experimental data). The model predicted with

accuracy over 98%. Model 2 captures the mass transport of the solvent from the

drug product, accounting for the temperature dependent mass transport of the

solvent.

Here K_{i} and C_{i}

are the diffusivity and concentration of solvent i, and T is the product temperature.

is calculated as a function

of temperature of each solvent in the system, based on the regression of the

experimental data.

Figure 1: Modeling approach for drying

To predict the optimal jacket temperature

profile for drying to meet the product requirements, the heat transfer model, Model

1 and Model 2 are solved using an algorithm like the one shown in Figure 2. For

a given temperature profile, and initial solvent levels, Model 2 predicted the

time evolution of the solvent concentration. At each time, Model 1 calculated

the Tg. The optimal input temperature profile was chosen such that the product

temperature at any time was lower than the predicted Tg at that time.

Figure 2: Modeling algorithm for

predicting the optimal temperature profile

The above model building approach

of combining machine learning and first principle modeling can be extended to

other unit operations as well where only part of the underlying physics is

understood. In combination with a mixing type of a model, this approach can be applied

to scale-up drying (Figure 1). The presentation shows specific example cases of

such applications.