Abstract
Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89–0.95) than regression models (0.56–0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easy-to-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.
Highlights
Paclitaxel is a potent mitotic inhibitor that is utilized for treating breast, lung and ovarian cancers, and Kaposi’s sarcoma (Weaver, 2014), so that it has been entitled the top-selling anticancer medicine in the world (Goodman and Walsh, 2001)
Goodness of fit displayed no difference regarding the accuracy of multiple linear regression (MLR) and backward regression for all output variables, 0.66, 0.56, 0.61, 0.58, and 0.85 for dry weight (DW), intracellular paclitaxel, extracellular paclitaxel, total yield of paclitaxel, and extracellular paclitaxel portion, respectively, for the training subset (Table 1)
Regression and multilayer perceptron-genetic algorithm (MLP-genetic algorithm (GA)) modeling were applied to evaluate the relationships among four studied factors “cell extract (CE) and culture filtrate (CF) concentration levels, elicitor adding day, and cell suspension culture (CSC) harvesting time” and the parameters “DW, intracellular, extracellular, and total yield of paclitaxel and extracellular paclitaxel portion”, and the possibility of predicting the growth and paclitaxel biosynthesis by the determined factors
Summary
Paclitaxel is a potent mitotic inhibitor that is utilized for treating breast, lung and ovarian cancers, and Kaposi’s sarcoma (Weaver, 2014), so that it has been entitled the top-selling anticancer medicine in the world (Goodman and Walsh, 2001). This impactful chemotherapeutic agent is used for off-label treatment of endometrial, gastroesophageal, prostate, cervical, and head and neck cancers (Weaver, 2014). The rising demand for paclitaxel and Taxus recalcitrant behavior under in vitro conditions have caused extensive effort toward finding alternatives for producing this invaluable secondary metabolite
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