A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes.

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed-batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647-1661, 2017.

Discovery of N-Aryl Piperazines as Selective mGlu(5) Potentiators with Efficacy in a Rodent Model Predictive of Anti-Psychotic Activity.

This Letter describes the discovery, SAR and in vitro and in vivo pharmacological profile of a novel non-MPEP derived mGlu(5) positive allosteric modulator (PAM) based upon an N-aryl piperazine chemotype. This mGlu(5) chemotype exhibits the ability to act as either a non-competitive antagonist/negative allosteric modulator (NAM) or potentiator of the glutamate response depending on the identity of the amide substituent, i.e., a 'molecular switch'. A rapidly optimized PAM, 10e (VU0364289), was shown to be potent and specific for the rat mGlu(5) receptor and subsequently demonstrated to be efficacious in a clinically relevant rodent model predictive of anti-psychotic activity, thus providing the first example of a centrally active mGluR(5) PAM optimized from an HTS-derived mGluR5 competitive antagonist.