1. Introduction:
The prevalence of diabetes in the United States has reached epidemic levels, with more than one-quarter of those aged 65 years and older suffer with the disease (Stailey et al., 2017 ;IDF Diabetes Atlas). Aging is a primary risk factor for type 2 diabetes mellitus (T2DM) (Ahima 2009 ), attributed to the concomitant increase in insulin resistance (Ahima 2009 ;Ayan and DeMirci 2023) and decline in pancreatic beta-cell function (Ahima 2009 ;Ayan and DeMirci 2023). T2DM in older adults may manifest with distinct clinical features, such as postprandial hyperglycemia (Taylor et al., 2021). However, the diagnosis of T2DM in older individuals may be complicated by the limitations of commonly used diagnostic tests, including hemoglobin A1C and fasting plasma glucose, which may fail to detect postprandial hyperglycemia (Korkiakangas et al., 2009 ). Moreover, older adults with T2DM are predisposed to a heightened risk of developing microvascular and cardiovascular complications, further underscoring the need for effective management and monitoring of the disease in this population (Vaidya et al., 2015). The therapeutic management of T2DM in older adults can pose limitations due to comorbidities, polypharmacy that is characterized by simultaneous use of multiple drugs, and renal or hepatic impairment (Dardano et al., 2014 ). While oral hypoglycemic agents are usually the preferred choice for the initial treatment of T2DM, the progressive deterioration of pancreatic beta-cell function may eventually require insulin therapy (Snyder et al., 2004). Nevertheless, administering insulin in older adults necessitates a judicious approach as this population is particularly susceptible to hypoglycemia and unawareness of its occurrence (Herman et al., 2005).
Basal-bolus therapy requires the administration of basal insulin referred as intermediate and long-acting insulins, prandial insulin, and correction doses as needed to replace endogenous insulin (Garg et al., 2010). Prandial insulin is administered to simulate the physiological response of endogenous insulin to food intake, which involves a rapid and vigorous first phase followed by a more prolonged second phase of insulin secretion into the portal circulation (Owens et al., 2001). Recent advances in insulin therapy, such as the development of insulin analogs, including rapid-acting insulin analogs (RAIAs), have transformed the management of type 1 diabetes (T1DM). RAIAs, such as insulin Aspart, Glulisine, and Lispro, offer superior pharmacokinetic and pharmacodynamic profiles compared to regular human insulin (RHI) (Ashwell et al., 2006;Hermansen et al., 2004;Becker et al., 2008). RAIAs may confer notable clinical benefits over RHI, including a lower risk of hypoglycemia and improved glycemic control (Garg et al., 2010).
Various strategies have been employed to express recombinant insulin inE. coli , including using different expression methods, tags, and host strains (Khalilvand et al., 2022 ). One efficient approach involves designing insulin constructs with a protease cleavage site using the home-made proteases to facilitate the cost-efficient production of mature insulin (Akbarian and Yousefi, 2018 ). In addition, host strains can be engineered to enhance insulin expression, such as the use of Rosetta™ 2 host strains, which are BL21 derivatives optimized for the expression of eukaryotic proteins with rare codons (Tegel et al., 2010 ). These strains carry a compatible chloramphenicol-resistant plasmid encoding tRNA genes for seven rare codons (AGA, AGG, AUA, CUA, GGA, CCC, and CGG) under their native promoters (Tegel et al., 2010 ). With IPTG induction, such strains are ideal for high-yield insulin production from target genes cloned into pET vectors (Pan and Malcolm, 2018).
During recombinant protein production, the chemical and nutritional composition of the culture medium can significantly impact host cell growth (Shokri et al., 2003). Factors such as carbon and nitrogen sources, metal ions, and medium pH play critical roles in determining cell growth, making it crucial to optimize the culture composition for high yields of the target protein (Kusuma et al., 2019;Nikerel et al., 2006). However, the sheer number of factors that can affect cell growth is vast, and evaluating their effects using the One-Factor-At-A-Time (OFAT) approach is time-consuming and labor-intensive (Abu et al., 2017). Furthermore, the OFAT approach must account for the potential dependent or independent effects and interactions between these factors (Abu et al., 2017). To address these limitations, the factorial approach offers a more comprehensive and efficient means of examining the impact of multiple factors on cell growth (Abu et al., 2017). By simultaneously evaluating all levels of all factors, this approach enables the determination of their independent effects and interactions (Abu et al., 2017). The Design of Experiment (DoE) is a statistical tool that employs fractional factorial models, such as Response Surface Methodology (RSM), to evaluate relevant interactions among variables via fewer experiments (Papaneophytou and Kontopidis, 2014 ;Sopyan et al., 2022;Dentener 2002). Furthermore, the BioLector XT high-throughput microbioreactor offers real-time monitoring of crucial cultivation parameters for aerobes and anaerobes, such as biomass, pH, dissolved oxygen in the liquid phase (DO), and fluorescence (Drummen;Osthege et al., 2022). This tool provides rapid and detailed insights into bioprocess development experiments, allowing for more efficient optimization of the culture medium and, ultimately, higher target protein yields (Osthege et al., 2022 ).
The objective of our study was to optimize the composition of the culture media to enhance the biomass of Rosetta™ 2 (DE3), which expresses a novel designer fast acting proinsulin. We employed theDesign-Expert (DoE) method, utilizing the BioLector XT system to support our investigations. By employing these sophisticated approaches we sought to identify a more comprehensive and efficient analysis of the various factors affecting the growth and yield of the modified insulin from Rosetta™ 2 (DE3); thus, we have been gain real-time insights into crucial cultivation parameters, enabling us to identify the optimal culture media composition to achieve maximum biomass production of the modified insulin. Accordingly, a screening experiment was conducted to assess the impact of various cultural components on the growth of Rosetta™ 2 (DE3), using the Plackett-Burman Design (PBD) and BioLector XT Microbioreactor. The optimal culture media composition was thereafter determined through further experimental design utilizing the BioLector XT Microbioreactor. The consequential significant factors were subsequently optimized, employing the Response Surface Methodology (RSM)Central Composite Design (CCD) of Design Expert suite. Following the completion of the optimization calculation, the obtained statistically relevant results were subsequently verified through experimental validation employing the BioLector XT Microbioreactor.