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Abstract
Paclitaxel as a microtubule-stabilizing agent is widely used for the treatment of a vast range of cancers. Corylus avellana cell suspension culture (CSC) is a promising strategy for paclitaxel production. Elicitation of paclitaxel biosynthesis pathway is a key approach for improving its production in cell culture. However, optimization of this process is time-consuming and costly. Modeling of paclitaxel elicitation process can be helpful to predict the optimal condition for its high production in cell culture. The objective of this study was modeling and forecasting paclitaxel biosynthesis in C. avellana cell culture responding cell extract (CE), culture filtrate (CF) and cell wall (CW) derived from endophytic fungus, either individually or combined treatment with methyl-β-cyclodextrin (MBCD), based on four input variables including concentration levels of fungal elicitors and MBCD, elicitor adding day and CSC harvesting time, using adaptive neuro-fuzzy inference system (ANFIS) and multiple regression methods. The results displayed a higher accuracy of ANFIS models (0.94–0.97) as compared to regression models (0.16–0.54). The great accordance between the predicted and observed values of paclitaxel biosynthesis for both training and testing subsets support excellent performance of developed ANFIS models. Optimization process of developed ANFIS models with genetic algorithm (GA) showed that optimal MBCD (47.65 mM) and CW (2.77% (v/v)) concentration levels, elicitor adding day (16) and CSC harvesting time (139 h and 41 min after elicitation) can lead to highest paclitaxel biosynthesis (427.92 μg l-1). The validation experiment showed that ANFIS-GA method can be a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis, as a case study.
Highlights
Plants are a rich source of active pharmaceutical components used in treatment of many diseases [1,2,3,4,5,6,7]
To model paclitaxel biosynthesis in C. avellana cell culture responding to fungal elicitor and MBCD by regression and adaptive neuro-fuzzy inference system (ANFIS) methods, fungal elicitor and MBCD concentration levels, fungal elicitor adding day and cell suspension culture (CSC) harvesting time were used as input variables, and paclitaxel biosynthesis as output variable
Various regression methods (MLR, stepwise regression (SR), ordinary least squares regression (OLSR), principal component regression (PCR) and partial least squares regression (PLSR)) were tested to find the best regression model to predict paclitaxel biosynthesis in C. avellana responding to fungal elicitors and MBCD
Summary
Plants are a rich source of active pharmaceutical components used in treatment of many diseases [1,2,3,4,5,6,7]. Some plant derived natural products (NP) such as aspirin have simple structure. Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture which could produce by chemosynthesis. Chemical synthesis of some valuable plant NPs e.g. paclitaxel is difficult because of their complex structure [8]. The extraction of NPs from intact plant limit the commercial production of these compounds owing to low yield, environmental restrictions and extinction risk of these valuable pharmaceutical sources [9, 10]. Using biotechnological approaches plant cell culture named “green cell factories” is a promising bioproduction platform to overcome these limitations and produce plant NPs on a large scale [1, 9, 11]
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