LUP Student Papers

Simulation, optimization and implementation of a twin-column MCSGP process for the purification of Liraglutide

• Mark

Abstract

A twin-column MCSGP process for the purification of Liraglutide was implemented at lab scale. This process is not a commercial separation step, but it is a model system with a realistic peptide mixture and with a non-commercial resin.
First, the process was modeled using a convective dispersive model to describe the concentration along the column and the steric mass action model to describe the adsorption rate. Then the parameters of the model were re-calibrated and validated in order for the experimental and simulated results to agree.
A multi-objective optimization with solvent productivity and yield as objective functions was performed, with three different purity requirements: 95 %, 98 % and 99 %. On the contrary, the loading length... (More)

A twin-column MCSGP process for the purification of Liraglutide was implemented at lab scale. This process is not a commercial separation step, but it is a model system with a realistic peptide mixture and with a non-commercial resin.
First, the process was modeled using a convective dispersive model to describe the concentration along the column and the steric mass action model to describe the adsorption rate. Then the parameters of the model were re-calibrated and validated in order for the experimental and simulated results to agree.
A multi-objective optimization with solvent productivity and yield as objective functions was performed, with three different purity requirements: 95 %, 98 % and 99 %. On the contrary, the loading length was kept constant at 0.25 column volumes (CV). The Pareto fronts, which consist of a group of optimal solutions, showed increased productivity and yield for lower purities because the requirement was less restrictive.
One solution was chosen based on good performance on both objective functions. This solution was successfully implemented at the lab, getting the same results as in the simulation. The purity was 99 %, the yield was almost 100 % and the solvent productivity was 6.77·10-3 g/L solvent. Although the purity and the yield were very good, the productivity can be improved even further by increasing the loading factor. This was done, first up to one CV and then eight CV of loading. For the latter, the purity was higher than 97 %, the yield was 99.6 % and the productivity was 0.243 g/L solvent or 5.792 kg/(hr·m3 column). (Less)

Abstract (Swedish)

En två-kolonn MCSGP process för upprening av Liraglutid genomfördes i laboratorieskala. Den här processen är inte en kommersiell uppreningsprocess, utan att den är ett modellsystem med en realistisk peptidblandning och med en icke-kommersiell harts.
Först modellerades processen med en konvektiv dispersiv modell för att beskriva koncentrationen genom kolonnen och den steriska massaktionsmodellen för att beskriva adsorptionshastigheten. Då kalibrerades modellens parametrar och validerades så att de simulerade resultaten passar bra ihop med de experimentella.
En multiobjektoptimering med produktivitet och utbyte som målfunktioner utfördes med tre olika renhetskrav: 95 %, 98 % och 99 %, medan laddningen hölls vid 0.25 kolonnvolymer (CV).... (More)

En två-kolonn MCSGP process för upprening av Liraglutid genomfördes i laboratorieskala. Den här processen är inte en kommersiell uppreningsprocess, utan att den är ett modellsystem med en realistisk peptidblandning och med en icke-kommersiell harts.
Först modellerades processen med en konvektiv dispersiv modell för att beskriva koncentrationen genom kolonnen och den steriska massaktionsmodellen för att beskriva adsorptionshastigheten. Då kalibrerades modellens parametrar och validerades så att de simulerade resultaten passar bra ihop med de experimentella.
En multiobjektoptimering med produktivitet och utbyte som målfunktioner utfördes med tre olika renhetskrav: 95 %, 98 % och 99 %, medan laddningen hölls vid 0.25 kolonnvolymer (CV). Paretofronter, som består av en grupp av optimala lösningar, visade ökad produktivitet och utbyte för lägre renhetsgrader eftersom kravet var mindre restriktiv.
En lösning valdes baserat på bra resultat av båda målfunktionerna. Den här lösningen genomfördes på labbet och samma resultat som i simuleringen uppnåddes. Renheten var 99 %, utbytet var nästan 100 % och produktiviteten var 6.77·10-3 g/L lösningsmedel. Även om renheten och utbytet var mycket bra, kan produktiviteten förbättras ytterligare genom att öka laddningsfaktorn. Det här gjordes först upp till en CV och sedan åtta CV av laddningen. När det gäller den senare, var renheten högre än 97 %, utbytet var 99.6 % och produktiviteten var 0.243 g/L lösningsmedel eller 5.792 kg/(hr·m3 kolonn). (Less)

Popular Abstract

Simulation, optimization and implementation of a twin-column MCSGP process for the purification of Liraglutide

The implementation of a MCSGP process led to higher productivity, yield and purity than in common batch chromatography for the purification of Liraglutide. To achieve that, the process was first optimized in order to give the best performance. This project was performed in collaboration with Novo Nordisk A/S.

Liraglutide is a medicine used in the treatment of type 2 diabetes. The number of people with type 2 diabetes is increasing throughout the world. Every 6 seconds, 1 person dies from diabetes and 12 % of global health expenditure is spent on diabetes, according to the International Diabetes Federation. Amongst all the... (More)

Simulation, optimization and implementation of a twin-column MCSGP process for the purification of Liraglutide

The implementation of a MCSGP process led to higher productivity, yield and purity than in common batch chromatography for the purification of Liraglutide. To achieve that, the process was first optimized in order to give the best performance. This project was performed in collaboration with Novo Nordisk A/S.

Liraglutide is a medicine used in the treatment of type 2 diabetes. The number of people with type 2 diabetes is increasing throughout the world. Every 6 seconds, 1 person dies from diabetes and 12 % of global health expenditure is spent on diabetes, according to the International Diabetes Federation. Amongst all the available medicines for the treatment of type 2 diabetes, Liraglutide has shown to be one of the best options with a number of advantages respect to its competitors. For those reasons, the demand of this medicine is expected to increase, and since the purification is the most expensive step, it is desired to increase productivity and efficiency in the purification of Liraglutide, which is based on chromatography.
The multicolumn countercurrent solvent gradient purification (MCSGP) is a semi-continuous process that allows recycling of part of the product that, in the common batch chromatography, is lost, increasing that way yield and productivity.
The implemented twin-column MCSGP process has shown to be able to achieve very high yield (99.6 %), high purity (97.3 %), and very good performance in terms of productivity (0.243 g/L solvent or 5.792 kg/hr·m3 column). In addition, it was successfully implemented at the lab getting the same results as in the simulation. It is important to remark that this process is not a commercial separation step, but it is a model system with a realistic peptide mixture and with a non-commercial resin.
Before implementing the process, it was modeled and calibrated so that the simulation fitted the experimental results, and then it was optimized to get the best process conditions.
The optimization was multi-objective, with yield and solvent productivity as objective functions. It was applied for three different purities: 95 %, 98 % and 99 %, and it was shown that for lower purities the yield and the productivity were higher, whereas for higher purities the yield and the productivity were decreased due to the need of a more restrictive pooling of the product.
The optimization for a batch process was also performed and compared with the MCSGP process. It resulted in a worse performance in MCSGP for purity 95 % and in an important improvement when the purity was 98 % and 99 %. This leads to the conclusion that the higher the required purity, the more favorable the MCSGP process is respect to the batch process. (Less)