L-asparaginase amidohydrolase (L-ASNase), also known as aminohydrolase pertains to the amidase group of enzymes (EC3.5.1.1), is widely recognized as one of the main anticancer drugs and a promising acrylamide mitigator in the food industry. This is due to its role in the hydrolysis of L-asparagine by a two-step mechanism where first the nucleophilic residue (Nuc) attacks the amide carbon atom of L-asparagine, releasing ammonia, thus generating a beta-acyl-enzyme intermediate. Subsequently, it acts on the ester carbon mediated by a water molecule, forming the L-aspartate molecule as shown in Figure 1 (Shakambari et al., 2019; Chand et al., 2020).

The foundation of ASNase-based treatments is the starvation of amino acids principle (Batool et al., 2016). Due to a mutation in the gene encoding L-asparagine synthetase, many leukemia or lymphoma cells cannot synthesize L-asparaginase and rely on its supply from plasma (Broome, 1968). As a result, leukemia cells undergo starvation and subsequent apoptosis due to altered signaling pathways caused by decreased plasma L-asparagine levels, which are hydrolyzed by L-ASNase (Ueno et al., 1997). This makes L-ASNase a key chemotherapeutic agent for the treatment of acute lymphoblastic leukemia (ALL), and lymphosarcoma. Additionally, its use has been reported in the treatment of acute myelomonocytic leukemia, critical lymphoblastic leukemia, melogenic leukemia, Hodgkin’s lymphoma, chronic lymphocytic leukemia, and more (Vimal et al., 2018). Moreover, L-ASNase has several non-medical applications, particularly as a mitigation agent for acrylamide, which is a known carcinogen (level 2 A) and an important neurotoxin (Wang et al., 2021). Acrylamide forms between reducing sugars, and amino acids (such as L-asparagine) when starchy foods are cooked at temperatures over 120°C, and under low humidity. This non-enzymatic mechanism is known as the Maillard reaction. The process involves the formation of a Schiff base and its subsequent decarboxylation. When this progression occurs under heat, either an ammonia or an imine molecule is eliminated, and subsequently replaced to form acrylamide (Jia et al., 2021). Consequently, when L-ASNase is administered, it hydrolyses the present L-asparagine and forms aspartic acid. As a result, the Maillard reaction cannot progress, and the formation of acrylamide is inhibited (Kornbrust et al., 2009). In 2016, the European Food and Drink Federation published a strategy called “Toolbox Acrylamide”, driven by industrial enzymes, which promotes the reduction of residual acrylamide in foods to protect public health.

L-ASNase is one of the therapeutic enzymes with the highest global production. It contributes 40% of the total global demand for enzymes with general use. In addition, it represents approximately one-third of the world’s requirement for antileukemic and antilymphoma agents. Thus it is one of the enzymatic products with major industrial potential (Vimal and Kumar, 2017). ln 2017, its global demand was approximately USD 380 m, and it is estimated to reach USD 420 m by 2025 (Alam et al., 2019). Currently, there are various formulations of L-asparaginase available in the market for clinical use, including those of bacterial origin such as native, PEGylated, and recombinant L-asparaginases from Escherichia coli (E. coli), as well as native L-asparaginases from Erwinia chrysanthemi (E. chrysanthemi). Additionally, fungal-derived asparaginases approved for food use, such as those from Aspergillus oryzae and Aspergillus niger, are also available (Battistel et al., 2021; Jia et al., 2021). All these formulations have been tested to improve their safety profiles (Burke, 2014; Effer et al., 2020; de Almeida Parizotto et al., 2021). However, Due to standard l-asparaginase preparations carries low thermostability, occurrence of side effects and restricted substrate specificity, these applications have been hampered by the diverse conditions frequently seen in the food and healthcare sectors (Zhang et al., 2021). Hence, it is of paramount importance to search for products that allow for the improvement of their properties, such as increased l-ASNase activity, reduced glutaminase activity, and stability for human physiological conditions in the therapeutic case, and improved L-ASNase activity and thermal stability for the food industry, while carefully considering the composition employed in their production, such as the immunological effects triggered by the bacterial-derived L-ASNase itself (Kishore et al., 2015; Wang et al., 2021).

Currently, several reviews have been attempted compiling L-Asparaginases from various sources that are able to improve both pharmacokinetics, reduce side effects and stability (Batool et al., 2016; Wang et al., 2021; Patel et al., 2022). Additionally, thanks to the principles of Quality by Design (QbD), several techniques have been proposed to overcome the disadvantages of the treatment, allowing the development of L-ASNases “bio-betters” (Brumano et al., 2019; Nunes et al., 2020). Each of these attempts aims to express these L-ASNases in heterologous hosts that allow them to be produced efficiently, economically, and easily. However, expressing a different or modified host protein from a different organism presents several challenges.

In this review, the main genetic modifications and current strategies developed to improve L-ASNase expression in microbial heterologous systems, including codon optimization, transcriptional regulation, promoter engineering, translation regulation, optimization of factors affecting expression, and host strains genetic and metabolic engineering (Figure 2), will be discussed. Special attention will be paid to E. coli, Bacillus subtilis and P. pastoris, which are the preferred hosts for L-ASNase expression. Finally, future challenges for the rational design of heterologous systems for L-ASNase expression are discussed.

L-ASNase is widespread, and can be found in animals, plants, and microorganisms such as bacteria, fungi, and yeasts, and even in various thermophilic organisms. Any of these organisms can be a potential source for L-ASNase production. However, not all sources are equivalent (Castro et al., 2021). L-ASNase has been conventionally classified according to amino acid sequence, the organism expressing it, inducibility, cellular localization, substrate affinity, and quaternary structure into three families: 1) bacterial, 2) plant, and 3) rhizobial (Michalska and Jaskolski, 2006; Loch and Jaskolski, 2021). However, this classification has been disputed due to the absence of the thermophilic group. According to Dumina et al. (Dumina et al., 2021), thermophilic L-ASNases differ from mesophiles both in their structural properties (they can be found in a hexameric form (Pritsa and Kyriakidis, 2001)), topological properties, deviation in the canonical arrangement of their active site, etc. For this reason, a new classification for thermophilic L-ASNases (class IV) proposed by the authors would be included in this review (Figure 3). Bacterial class L-ASNases can be subdivided into two different isozymes: Type I and II L-ASNases. Type I L-ASNases (EcA I) is a homodimeric constitutive enzyme located in the cytoplasm, with low affinity for L-asparagine and high affinity for L-glutamine (Pokrovskaya et al., 2022). Among the Type I enzymes, L-ASNases produced by B. subtilis (Jia et al., 2013; Feng et al., 2017; Jia et al., 2021; Niu et al., 2021), Thermococcus kodakarensis (Chohan and Rashid, 2013; Hong et al., 2014), and Acinetobacter soli (Jiao et al., 2020) are the most studied examples. Type II bacterial L-ASNases (EcAII), normally a homotetrameric form and located in periplasmic space with expression induced during anaerobiosis and are secreted only when bacteria are exposed to low nitrogen concentrations (Verma et al., 2007). Their properties have been discussed in E. coli, Erwinia carotova, Erwinia chrysantemi, Saccharomyces cerevisiae, etc (Yano et al., 2008). Although both isozymes exhibit enzymatic activity for l-asparagine and l-glutamine, their affinity for L-asparagine is what distinguishes them from one another. Since EcAII has a higher specific affinity for l-asparagine, which results in high antitumor activity and is therefore the one used in medicinal applications (Sharafi et al., 2017). To get an idea, the EcAII K _M (Michaelis-Menten constant) = 10–15 µM versus EcAI K _M = 3.5 mM. This means that enzymes EcAII display much higher (2+ orders of magnitude) affinity for L-asparaginase than EcAI (Nunes et al., 2020). Microbial enzymes, like EcAII, are more suitable than their animal and plant counterparts as they provide a consistent profile, stability, relative ease of production and purification, High yields and consistency; simplifying the modification and optimization of the manufacturing process (Lopes et al., 2017; Vachher et al., 2021).

Commercially, there are two products currently available for acrylamide mitigation in the food industry. These are PreventASe TM from DSM (Heerlen, Netherlands) and Acrylaway^® from Novozymes A/S (Bagsvaerd, Denmark). PreventASe TM was the first, launched in 2007. It was obtained after analyzing the gene sequence of Aspergillus niger. It has an acidic profile (optimum pH 4-5, temperature 50°C). Acrylaway^®, on the other hand, is obtained from Aspergillus oryzae and has a near-neutral profile (pH optimum 7, temperature 37°C) (Xu et al., 2016). As for its safety, it has been observed over the years that there are no hazards in its use and these are recognized as safe by the U.S. government and are currently used in several countries, including the U.S., Australia, China, Russia, Mexico, etc. (Xu et al., 2016). Additionally, several products are currently available for anti-leukemia treatment. Elspar^®, Oncaspar^® (Pegaspargase), Crisantaspase^®, Kidrolase^®, and Erwinase^® or Erwinaze^® are some of the commercially available brands of ASNase. Elspar^® contains L-asparaginase derived from E. coli. Oncaspar^® is a modified version of Elspar^® obtained by covalent conjugation of E. coli asparaginase with monomethoxypolyethyleneglycol (PEG), to increase the plasma half-life and decrease the immunogenicity and antigenicity of L-asparaginase. However, a higher prevalence of side effects has been observed (Galindo-Rodríguez et al., 2017). Crisantaspase^® and Erwinase^® are obtained from Erwinia chrysanthemi. The former is often used in combination with other anticancer drugs, while the latter is used in conjunction with chemotherapy or radiotherapy as part of treatment protocols. Escherichia coli Kidrolase^® is used in the treatment of ALL, leukemic meningitis, and non-Hodgkin’s lymphoma (Izadpanah Qeshmi et al., 2018). All these formulations have been tested to improve their safety (Burke, 2014; Effer et al., 2020; de Almeida Parizotto et al., 2021; Munhoz Costa et al., 2022).

Nevertheless, microbial L-ASNase II presents several problems when administered as an antileukemic drug, including severe allergic reactions, nausea, diabetes, pancreatitis, and venous thromboembolism (Goyal and Bhatt, 2015; Schmiegelow et al., 2016).

In recent years, several biological sources of L-ASNase able to tackle some of the mentioned issues through engineering have been explored. These endeavors have yielded L-ASNases with. 1) reduced immunogenic activity or allergic reactions, 2) high catalytic activity, and 3) low-cost up and downstream processing (Patel et al., 2022). It has been observed that L-ASNases in eukaryotes, such as fungi and yeasts, can result in enzymes with fewer adverse effects and advantageous characteristics (Cachumba et al., 2016). For example, L-ASNase I from the yeast Saccharomyces cerevisiae (ScASNaseI) and expressed in E. coli BL21 (DE3) has been studied (Munhoz Costa et al., 2016). This is because it was predicted to be a bacterial type II isoform, being a possible candidate as an antileukemic agent. The specific activity of L-asparagine in ScASNaseI was 196.2 U/mg, a value similar to the commercial ASNase activity of E. coli (223 U/mg). This enzyme maintains allosteric behavior and localization in the cytosol of the enzyme, as in the case of type I enzymes, but with a k _M of 75 µM as in type II enzymes. In addition, they performed specific activity tests for L-glutamine, presenting 0.38% of L-asparaginase activity, and cytotoxicity tests on MOLT-4 leukemia cells, killing 85% of the cells under physiological conditions (pH 7.4 at 37°C), with optimal activity at pH 8.6 and at 40°C. This enzyme is compatible with the treatment of leukemia. However, alternative sources to bacteria and fungi are currently being explored (Patel et al., 2022). For example, marine microorganisms would produce L-ASNases capable of withstanding pH, salinity, and pressure conditions similar to those of blood plasma. (Qeshmi et al., 2018). A study focused on the cloning, expression, and characterization of L-asparaginase from marine Pseudomonas aeruginosa HR03 isolated from fish intestines in E. coli BL21 (D3) as a host. The recombinant L-asparaginase (HR03Asnase) was purified and its enzymatic properties were determined. The maximum activity of the enzyme was observed at 40°C and pH 8. The study suggests that HR03Asnase has potential for commercial applications in the food and health industries (Izadpanah Qeshmi et al., 2022). Additionally, thermophilic microorganisms would have been studied which can produce L-ASNases that remain stable at high temperatures, which are potentially suitable for the food industry (Dumina and Zhgun, 2023). A study, identifies a new thermostable L-asparaginase from Pyrococcus yayanosii CH1 expressed in B. subtilis 168. This L-ASNase was characterized by obtaining a maximum volumetric yield of 1483.81 U/mg, a maximum activity at 95°C and pH 8, making it suitable for industrial food application (Li et al., 2018).

In addition to this, several techniques have been proposed to overcome the disadvantages of native ASNases, improving it obtaining novel bio-betters ASNases. The term bio-better, refers to creating novel drugs by enhancing the features of current peptide- or protein-based biopharmaceuticals, such as affinity, selectivity, immunogenicity, and stability against degradation of proteases (Beck, 2011; Lagassé et al., 2017). These proteins are manufactured from molecular and/or chemical modifications of an original product to improve drug characteristics (Courtois et al., 2015). Molecular strategies like protein engineering by bioinformatics analysis, docking, molecular dynamics and site-directed mutagenesis have been mentioned among the most sophisticated techniques (Nguyen et al., 2016a; Ardalan et al., 2018; Mundaganur et al., 2014; Munhoz Costa et al., 2022). An investigation, by means of directed evolution methodology, succeeded in obtaining a double mutant ASNase from E. chrysanthemi expressed in E. coli BL21 (DE3). This mutant L-ASNase, besides having a specific activity 46% higher than the wild type L-ASNase, also presents a reduction of the glutaminase activity by 40% and a decrease of the immunogenic effect of 62.5%, being this a promising enzyme in the pharmaceutical industry (Munhoz Costa et al., 2022). Chemical modifications such as scFv-fusion, TRAIL domain-fusion, albumin binding-fusion, PEGylation, PASylation and bioconjugations have been employed (Guo et al., 2000; Abribat, 2023; Trieu, 2010; Lavie and Nguyen 2017; Brumano et al., 2019). A chemical modification of a commercial biosimilar E. coli L-ASNase (Leunase^® (Kyowa Hakko Kirin, Japan)) was studied by direct conjugation of carboxyl groups to primary amines by 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) (Chahardahcherik et al., 2020). In this case, a polymer called carboxymethyl dextran (CMD) was used, which is biologically compatible. The results showed a substantial increase in the specific activity of the modified L-ASNase compared to the commercial one (1609.62 vs 629.8 U/mg). Additionally, an increase in half-life stability in rat serum of 192 h with the modified L-ASNase versus 96 h with the native one, and an improvement in temperature and pH stability were observed. In recent years, bio-better proteins have gained considerable industrial attention, as they are patentable and have higher prices in the market due to their clinical advantages (Brumano et al., 2019).

For large-scale processes, biopharmaceutical production from wild strains host is generally avoided. This is mainly due to low yield and high production costs. To overcome the problems faced by conventional L-ASNase production, one approach would be to use recombinant DNA technology, to transfer genes that encode the enzyme, from one microorganism to another. This is called heterologous expression (Li et al., 2019). Heterologous expression allows the relatively stable, safer expression of enzymes, with higher yields (Patel et al., 2022). There are several expression systems available for biopharmaceutical purposes, including bacteria, yeast, filamentous fungi, mammalian cells, plants, insects, transgenic animals, and even microalgae (dos Santos et al., 2018). Each system has its particular features in terms of production capacity, costs, safety, complexity, and specific processing (Schmidt, 2004). The use of complex and costly expression systems, such as mammalian cells (CHO, insects, etc.), are generally used for proteins that require complex post-translational modifications, which in the case of L-ASNase are not necessary. Concerning the use of plant-based expression systems, these display several disadvantages due to the large numbers of proteases present in their cells, making extraction and purification challenging for large-scale enzyme production (Patel et al., 2022). The high secretors and the host strains of bacteria (e.g., E. coli, Bacillus and lactic acid bacteria), filamentous fungi (e.g., Aspergillus) and yeasts (e.g., Pichia pastoris) are most commonly used for the homologous and heterologous expression of recombinant enzymes without complex post-translational modifications (Goswami et al., 2015). Among these, E. coli, B. subtilis and P. pastoris are used for the production of L-ASNase, since these can quickly and easily overexpress (Wang et al., 2021; Patel et al., 2022). However, the yields of L-ASNase production depend not only on host selection. But also of the fermentation process, and the efficiency of the expression systems (Li et al., 2019). The requirements for a successful high-throughput process for protein production as: 1) high transcription, and translation of genes of specific protein, 2) correct folding, and induction that does not cause stress to the host, 3) desired post-translational modifications, 4) efficient secretion, and limited or no degradation of the product in the culture medium (Kaur et al., 2018).

As previously mentioned, the design of an efficient bioprocess strategy is essential for a profitable industry of clinically relevant recombinant proteins. Heterologous protein expression using genetically modified prokaryotic hosts has made it possible to provide a wide range of recombinant proteins. This production, would not be feasible without this technology, as the wild-type cells are not prepared to provide it, in a scalable and rentable manner (Kim et al., 2020). Yet, there are challenges and limitations in the use of these systems. Generally, heterologous expression of proteins has various problems, such as: inadequate folding, heavy molecular weight, or the presence of multiple membrane domains in the protein; cellular metabolic burden, codon usage differences, and sequence repetitiveness that affect translation. For example, one study reported that the heterologous expression of l-asparaginase from Rhizomucor miehei in Pichia pastoris resulted in low protein expression levels and low enzyme activity due to suboptimal transcriptional and translational regulation (Zhang et al., 2021). Effer et al. (Effer et al., 2019) discusses the evaluation of extracellular expression into P. pastoris Glycoswitch VR using two different plasmid constructions containing the asnB gene (encoding for L-ASNase of Erwinia chrysanthemi), with and without His-tag, to find the best system for producing the extracellular and biologically active protein. The study found that the His-tag could negatively affect the tetrameric conformation of L-asparaginase and possibly affect proper protein folding. This could lead to most of the proteins being accumulated for degradation through ER-associated degradation (ERAD), resulting in low extracellular L-asparaginase production. Another study discusses the effect of hydrophobic region on the signal peptide on L-asparaginase secretion and inclusion bodies (IB’s) formation in E. coli (Naderi et al., 2022).

Results showed that increasing hydrophobicity of the signal peptides did not necessarily improve secretion efficiency, and in some cases, increased IB’s formation. IB’s are insoluble protein aggregates generated by the metabolic burden that cells undergo upon induction (Vallejo and Rinas, 2004). The problem of IB’s formation or misfolding is further aggravated in the case of L-ASNase. This is mainly because L-ASNase II is fully active in its tetrameric form. This is because the active-site pocket consists mainly of one protomer and is complemented by several residues from the second protomer within a compact dimer (Lubkowski and Wlodawer, 2021) (Figure 4). Therefore, when IB or misfolding occurs, it is required to refold into its native form to maintain its bioactive properties (Mihooliya et al., 2022). Considerable effort has gone into downstream processing involving isolation, solubilization, renaturation (refolding), and purification to obtain the soluble, bioactive protein (Kante et al., 2018). Researchers have achieved up to 50% recovery of functional L-ASNase using various strategies such as the use of strong chaotropic agents (Kante et al., 2018), pulse dilution method (Upadhyay et al., 2014), Freeze-Thaw method (Singhvi et al., 2021), refolding in periodic counter-current chromatography (PCC) (Rajendran et al., 2022), among others. However, these steps are time consuming; require major equipment such as new generation chromatographs, ultrafiltration and diafiltration systems, hydraulic intensifier systems, etc; a large number of reagents such as chaotropic agents, micelles, liposomes, detergents, etc; and use large volumes (generally 1–10 L for mg quantities of protein) (Clark, 2001; Singh et al., 2015; Yuan et al., 2015). Additionally, these strategies are tedious and require large amounts of steps, even more in the case of multimeric proteins, because it requires first a correct renaturation and solubilization of the inactive monomers, for their subsequent refolding of their tetrameric structure under various physiological conditions (Upadhyay et al., 2014; Mihooliya et al., 2022).

Hence, the rational design of vector systems that include the optimization of codons, transcription regulation, optimization of promoters, translation regulation, the optimization of factors that affect expression in the soluble fraction, co-expression of molecular chaperones and secretion strategies, would bolster the production of the proteins of interest (Juturu and Wu, 2018; Kim et al., 2020; Kant Bhatia et al., 2021) (Figure 5). Table 1 presents the production of heterologous L-ASNase from several sources, together with its expression systems, reviewed in this article.

One of the many approaches to improve the solubility of recombinant proteins, is to slow down the protein synthesis process, thereby allowing sufficient time for the protein to reach its native structure. Some of the strategies for this purpose include using weak promoters, low concentration of inducer and low cell culture temperature (Kant Bhatia et al., 2021). However, all these strategies have the problem of low yield of proteins. Other approaches include the use of genetic engineering to optimize factors such as the co-expression of chaperones and the formation of disulfide bonds, the use of fusion tags, and the translocation of proteins to the extracellular medium (Singha et al., 2017).

The choice of host strain can also play a crucial role in the successful protein production process. The selection of strain should primarily consider the requirements of the plasmid expression system, including: 1) the type of polymerases necessary for protein expression, 2) compatibility between available tRNA anticodons and codons of the heterologous gene, 3) stability of the plasmid or protein within the strain, 4) proper protein capability folding within the strain, 5) requirements for posttranslational modifications, and 6) potential toxicity of the protein to the strain itself (Makino et al., 2011). Nowadays, advancements in genetic and metabolic engineering have enabled the modification of organisms to improve their recombinant protein expression levels. The subsequent secretions will discuss the strategies employed in host strain engineering and metabolic engineering that have been utilized to improve the heterologous expression of L-ASNase.

To achieve successful L-ASNase production, it is crucial to employ a rational approach in designing expression systems, selecting appropriate strains, and making genetic or metabolic modifications. While various strategies have been explored, such as strain engineering, metabolic engineering, and bioinformatics tools, there is still room for improvement in L-ASNase production. Cutting-edge computational, such as In Silico Optimization (ISO) tools, are being adopted to enhance the process. These tools utilize computational methods like simple algorithms, dynamic programming, statistical techniques, and machine learning algorithms (such as artificial neural networks, support vector machines, and deep learning) to generate comprehensive models, reducing the need for time-consuming in vitro experimentation (Packiam et al., 2020), In Silico Optimization (ISO) tools use appropriate computational methods to generate models based on these approaches, which allow tackling the optimization with a broader and integrative view. Thus, avoiding in vitro experimentation and in the process, speeding up the workflow. Examples of computational methods include: 1) simple algorithms, 2) dynamic programming, 3) statistical methods, and iv) ISO tools offer several benefits for optimizing gene expression, allowing modifications based on the host. They evaluate and adjust gene properties like codon usage, GC composition, mRNA stability, cryptic splice sites, and premature polyadenylation signals (Watts et al., 2021; Vasina et al., 2022). Notable tools in this area include “SignalP” and “Phobius,” which predict the most efficient signal peptide for a given amino acid sequence, thereby saving time and resources by eliminating the need for constructing multiple vectors (Zhou et al., 2018). Another approach involves using the “nondominated sorting differential search algorithm and flux balance analysis (ndsDSAFBA)", a multi-object optimization model that leverages in silico metabolic pathway models to enhance metabolite production. This approach offers a less labor-intensive and cost-effective methodology (Daud et al., 2019) Additionally, deep neural networks have been applied, such as the “mutation predictor for enhanced protein expression (MPEPE)", which can suggest amino acid sequence mutations to improve protein expression (Ding et al., 2022). Moreover, machine learning (ML) is being employed as a rational design strategy, exemplified by the development of MALLPHAS, a strain engineering tool that optimizes protein secretion (Markova et al., 2022).

Another recent strategy involves the use of CRISPR-Cas-based gene editing tools to enhance recombinant protein expression (Gu et al., 2018; Baghban et al., 2019; Fontana et al., 2020). CRISPR has been employed as a synthetic promoter activator for optimizing protein expression. Additionally, small molecule-sensitive gRNAs have been utilized to regulate gene expression in E. coli and precisely control multigene pathways (Fontana et al., 2020). The application of CRISPR-Cas9 for genomic engineering in yeast, including P. pastoris, has been reported, enabling rapid and marker-free modifications for strain and metabolic engineering purposes (Weninger et al., 2016). B. subtilis is another microorganism benefiting from CRISPR technology, with the development of a CRISPR-Cas9 toolkit for comprehensive engineering. This toolkit addresses challenges like low editing efficiency, complex cloning processes, and limited multiplexing capacity, thereby advancing the engineering capabilities of this strain (Gu et al., 2018). The combination of CRISPR and machine learning (ML) facilitates the maturation of metabolic engineering. CRISPR technology enables modifications at numerous genomic sites, simplifying gene editing and metabolic perturbations. ML, on the other hand, aids in the rational selection of optimal genes for desired products or applications through predictions and recommendations (Lawson et al., 2021).

The future challenges in optimizing heterologous protein expression involve integrating the aforementioned bioinformatics tools and designing tools capable of optimizing expression systems comprehensively, from transcriptional regulators to transcription termination. Furthermore, emerging technologies like CRISPR and ML hold promise in facilitating optimization and enhancing the reliability of predictions. In the coming years, the synergy between new algorithms and biotechnological tools should enable the development of advanced software and methodologies that can significantly reduce the time and costs associated with pharmaceutical products like L-ASNASA.

Overall, this review provides an overview of molecular and metabolic strategies that can be used to optimize heterologous expression of L-ASNase. This article describes several approaches that have been employed for this means, including the use of molecular tools, strain, and metabolic engineering, and in silico optimization. Through a clear and insightful analysis, it highlights the need for a rational design approach to achieve successful expression. In addition, it acknowledges the challenges to large-scale production of bio-betters L-ASNases.

In summary, the use of genetic engineering, rational design of heterologous expression systems, metabolic strategies, would allow motivating and facilitating the pharmaceutical industry to continuously innovate product manufacturing processes, and develop new treatments effectively and using Good Manufacturing Practices (GMP). In this sense Brumano et al. (Brumano et al., 2019), mentions that the development of bio-betters L-ASNases begins with the development of the process. Therefore, the search for hosts and expression systems that facilitate upstream and downstream processes such as 1) expression systems and the use of bioinformatics tools that allow codon optimization such as “Twist codon optimization” and RBS such as “RBS calculator v2. 0”; 2) hosts capable of producing l-ASNases from new microbiological or molecularly and/or chemically modified sources; 3) enzyme secreting microorganisms such as B. subtilis or P. pastoris strains; 4) expression systems capable of stabilizing structure conformation and solubility such as the use of pET-SUMO systems or co-expression of chaperones; and 5) that manage to improve L-ASNase production yields such as promoter optimization have been reviewed in this work. These strategies would guarantee the efficacy and safety of the final product that L-ASNase producing industries seek for a continuous improvement of both the process and the product. For this reason, it is of utmost importance to develop a strategy to address all the points mentioned in this review. In Figure 6 it proposes a workflow that would allow an effective rational design of the host and expression system to produce biologically improved L-ASNase.