Over the past several decades, mRNA vaccines have evolved from a theoretical concept to a clinical reality. These vaccines offer several advantages over traditional vaccine techniques, including their high potency, rapid development, low-cost manufacturing, and safe administration. However, until recently, concerns over the instability and inefficient distribution of mRNA in vivo have limited their utility. Fortunately, recent technological advancements have mostly resolved these concerns, resulting in the development of numerous mRNA vaccination platforms for infectious diseases and various types of cancer. These platforms have shown promising outcomes in both animal models and humans. This study highlights the potential of mRNA vaccines as a promising alternative approach to conventional vaccine techniques and cancer treatment. This review article aims to provide a thorough and detailed examination of mRNA vaccines, including their mechanisms of action and potential applications in cancer immunotherapy. Additionally, the article will analyze the current state of mRNA vaccine technology and highlight future directions for the development and implementation of this promising vaccine platform as a mainstream therapeutic option. The review will also discuss potential challenges and limitations of mRNA vaccines, such as their stability and in vivo distribution, and suggest ways to overcome these issues. By providing a comprehensive overview and critical analysis of mRNA vaccines, this review aims to contribute to the advancement of this innovative approach to cancer treatment. Cancer immunotherapies have gained significant attention and popularity in recent years, significant advancements have been made in cancer immunotherapies with the FDA approval of checkpoint blockade modulators (such as pembrolizumab in 2014 and nivolumab in 2015) and CAR-T cell immunotherapies (like tisagenlecleucel in 2017 and axicabtagene ciloleucel in 2018) [The primary goal of cancer immunotherapies is to enhance the host's anti-tumor immunity and modify the tumor's suppressive microenvironment. By doing so, these therapies aim to inhibit the growth of the patient's tumor and prolong their lifespan . ThroughCancer vaccines have emerged as a promising alternative approach to cancer immunotherapy, with potential applications in cancer prevention and therapy. Unlike traditional vaccines that are used to prevent infectious diseases, cancer vaccines are designed to stimulate the immune system to recognize and attack cancer cells. Cancer vaccines may be used as a preventative measure in individuals at high risk for developing certain types of cancer, or as a therapeutic option to treat existing cancer. The development of cancer vaccines has the potential to revolutionize cancer treatment by offering a more targeted and personalized approach, with fewer side effects than conventional cancer treatments. The application of cancer vaccines in both cancer prevention and therapy highlights their potential as a promising tool in the fight against cancer . VaccinaImmunologic memory, a property of the immune system, plays a crucial role in the effectiveness of cancer vaccines. This memory allows the immune system to recognize and attack cancer cells even after the initial exposure to a cancer vaccine. Unlike other types of immunotherapies, cancer vaccines offer a specific, non-toxic, and well-tolerated therapy option. By targeting cancer-specific antigens, cancer vaccines have the potential to induce a targeted immune response, reducing the risk of adverse effects and toxicities associated with traditional cancer treatments. The specific nature of cancer vaccines allows for a personalized approach to cancer treatment, targeting the specific antigens expressed by a patient's cancer cells. This personalized approach, along with the non-toxic and well-tolerated nature of cancer vaccines, highlights their potential as a valuable tool in cancer immunotherapy . DespiteThis is the case despite the fact that there have been significant attempts to generate cancer vaccines. Despite the fact that the human papillomavirus (HPV) is responsible for 70% of cervical cancers and the hepatitis B virus may cause liver cancer, the Food and Drug Administration in the United States has only recently licenced two prophylactic vaccines. More encouragingly, the first therapeutic cancer vaccine, PROVENGE (sipuleucel-T), was licensed by the U.S. FDA  which waHLA (human leukocyte antigen) molecules play a critical role in presenting antigens to the immune system . HoweverNucleic acid vaccines are risk-free for use in both preventive and therapeutic contexts since they do not transmit infections and their production does not include any protein or virus-derived contaminations . In receThese advantages include decreased production costs and the possibility for wider use. In terms of cancer treatment, clinical trials have mostly concentrated on non-replicating mRNAs up to this point . Self-amThis review article covers a range of topics related to mRNA vaccines in cancer immunotherapy. It begins with a discussion of basic mRNA vaccine pharmacology and recent advances in mRNA vaccine technology. The article then examines the optimization of mRNA translation and stability, modulation of immunogenicity, and progress in mRNA vaccine delivery. Various delivery methods are discussed, including ex vivo loading of DCs, injection of naked mRNA in vivo, physical delivery methods in vivo, protamine, and cationic lipid and polymer-based delivery. The review covers the development of mRNA cancer vaccines, including DC mRNA cancer vaccines and direct injection of mRNA cancer vaccines. It also highlights therapeutic considerations and challenges, good manufacturing practice production, and regulatory aspects of mRNA vaccines. Strategies to improve mRNA translation efficiency and overcome innate immunogenicity are examined, including the modification of the five-prime cap, optimization of untranslated regions, codon optimization of open reading frames, poly(a) tail modification, nucleoside modified mRNA, and purification of IVT-mRNA. The article also discusses the immunogenicity of mRNA and paradoxical effects in cancer immunotherapy, as well as self-amplifying mRNA vaccines, their structure, advantages, and deliveries. The review covers the delivery of mRNA cancer vaccines, including the rationale for lipid nanoparticles to maximize delivery efficiency and immunogenicity, mechanistic studies, and additional functional modifications of LNPs, LNP mRNA vaccine from formulation to manufacturing, polymer-based mRNA delivery systems, peptide-based mRNA delivery systems, and other formulations used in mRNA delivery. The article also examines the injection routes of mRNA cancer vaccines and provides a clinical overview of mRNA cancer vaccines. Finally, the review discusses mRNA encoding immunostimulants, mRNA vaccine encoding tumor-associated antigens, mRNA vaccine encoding neoantigen, personalized vaccines, and concludes with future perspectives on the development of RNA-based treatments in cancer immunotherapy.Cancer immunotherapy is a revolutionary approach to treating cancer that harnesses the power of the immune system to recognize and destroy cancer cells . The immImmune checkpoint inhibitors are a type of cancer immunotherapy that target molecules known as checkpoints on immune cells . These cImmune checkpoint inhibitors work by blocking these inhibitory signals, thereby unleashing the immune system's ability to recognize and attack cancer cells. Specifically, these inhibitors bind to either PD-1 or CTLA-4, preventing the cancer cell's ligands from interacting with the checkpoint molecules . As a reCAR-T cell therapy is an innovative form of cancer immunotherapy that involves modifying a patient's own T cells to enhance their ability to recognize and attack cancer cells . The proCAR-T cell therapy represents a personalized and highly targeted approach that harnesses the power of the patient's immune system to fight cancer. Once infused back into the patient, the CAR-T cells multiply and persist in the body, allowing for a sustained anti-tumor response . The CARCAR-T cell therapy has revolutionized the field of cancer treatment, particularly for certain types of blood cancers . It has CAR-T cell therapy is continually evolving and improving. Researchers are exploring ways to enhance its effectiveness and reduce side effects. One area of focus is the development of "second-generation" and "third-generation" CARs that incorporate additional signaling domains to enhance CAR-T cell activation and persistence . These aTumor-infiltrating lymphocyte (TIL) therapy is a form of cancer immunotherapy that harnesses the power of a patient's own immune system to fight against cancer . In TIL TIL therapy also has the potential to modify the tumor microenvironment by increasing the infiltration of immune cells into the tumor, thereby creating a more hostile environment for cancer cells and improving the overall anti-tumor immune response . TIL theTIL therapy not only focuses on the direct elimination of cancer cells but also aims to modify the tumor microenvironment . Tumors Ongoing studies are focused on optimizing TIL expansion techniques, improving the selection of tumor-specific TILs, and exploring combination therapies to enhance the efficacy of TIL-based treatments. In addition to its potential as a standalone therapy, TIL therapy is also being investigated in combination with other cancer treatments . For exaOne such challenge is the limited availability of TILs from some tumor types or patients with low TIL infiltration. Researchers are exploring strategies to generate and expand TILs from small tumor samples or using techniques such as genetic engineering to improve TIL functionality . AnotherAs research in TIL therapy progresses, it holds the potential to become an integral part of the cancer treatment landscape . By harnTherapeutic vaccines can be developed using different strategies. One approach is to use tumor-specific antigens derived from the patient's own tumor cells . These aTherapeutic vaccines are typically administered through injections, either subcutaneously or intramuscularly. The vaccination process may involve multiple doses over a period of time to optimize the immune response . In someTherapeutic vaccines offer several advantages in the field of cancer immunotherapy . One majAdoptive cell transfer is a cancer immunotherapy approach that involves transferring immune cells, such as TILs or genetically modified T cells, into the patient . The proAdoptive cell transfer holds promise as a powerful tool in cancer treatment, harnessing the potential of the immune system to fight against cancer. Through adoptive cell transfer, the immune cells that are transferred into the patient are specifically selected and engineered to recognize and target cancer cells . This apThe translation of protein-encoding DNA into mRNA is the first step in the synthesis of proteins by ribosomes in the cytoplasm. Figure Both non-replicating and virally produced, self-amplifying RNA are now being investigated as vaccine candidates: non-replicating RNA and self-amplifying RNA. The antigen of interest and the 5′ and 3′ UTRs are encoded by conventional mRNA-based vaccines, whereas self-amplifying RNAs encode not only the antigen of interest but also the viral replication machinery, which allows for intracellular RNA amplification and abundant protein expression. RNA polymerase  is used to transcribe the linear DNA template into mRNA, which is then used to transcribe the mRNA in vitro. The final product should include an open reading frame that encodes the protein of interest, flanking UTRs, a 5′ cap, and a poly(A) tail, if possible. As a result, the mRNA has been made to look and behave like fully processed mature mRNA molecules seen in the cytoplasm of eukaryotic cells in the natural world. Unprocessed mRNA is rapidly destroyed by extracellular RNases and does not undergo effective internalization. The result has been the development of a large number of different in vitro and in vivo transfection agents that aid in the absorption of mRNA into cells while also protecting it from destruction.Once the mRNA reaches the cytosol, the cellular translation machinery begins to generate protein, which is then subjected to post-translational modifications, culminating in a correctly folded and completely functioning protein. As previously stated, this aspect of mRNA pharmacology is especially helpful for vaccinations and protein replacement treatments that need the delivery of cytosolic or transmembrane proteins to the appropriate cellular compartments in order to be effective. In the end, IVT mRNA is destroyed by natural physiological processes, lowering the risk of toxicity from metabolites. Figure Following translation, the synthesized protein undergoes post-translational modifications and may either function within the producing cell or be secreted to act through autocrine, paracrine, or endocrine pathways. In immunotherapeutic applications, the protein is degraded into antigenic peptide epitopes and loaded onto major MHC molecules to elicit immune responses. MHC class I and class II molecules present antigens to immune effector cells, with class II molecules typically requiring additional routing signals encoded into the mRNA. Dendritic cells (DCs) play a crucial role in this process by processing exogenous antigens and cross-priming. Ultimately, the immune system recognizes and responds to the antigens presented on the cell surface by both MHC class I and II molecules.In recent years, a number of mRNA vaccine platforms have been generated and confirmed in investigations of immunogenicity and effectiveness . ThroughThe development of highly effective and non-toxic RNA carriers has allowed for the expression of antigens in vivo to be extended in certain circumstances . Novel aStability and translation of mRNA are significantly influenced by the 5′ and 3′ UTRs that surround the coding sequence, both of which are key issues for vaccine development . RegulatExogenous mRNA may be recognized by the innate immune system at several levels, including the cell surface, the endosome, and the cytosol ; as a rePurifying IVT mRNA, adding various nucleosides, and complexing the mRNA with other carrier molecules are all possible ways to modify the immunostimulatory profile of the mRNA, as was discovered in recent study . It is pAccording to the findings of a study that was carried out by some researchers, sequence-optimized, HPLC-purified, unmodified mRNA produced greater amounts of protein in HeLa cells and in mice than its nucleoside-modified counterpart did . In addiIn order to achieve therapeutic relevance, effective mRNA distribution in living organisms is required . It is nDCs are unparalleled in their ability to deliver antigens to T cells . AdaptivConsidering these characteristics, DCs are a potentially effective in vivo and ex vivo target for mRNA vaccine transfection . While iIn vivo vaccines using naked mRNA have been shown to be effective, particularly when administered by intradermal or intranodal injections, both of which preferentially target antigen-presenting cells . Recent High-throughput sequencing is used to identify each somatic mutation in a patient's tumor sample . The terTumor growth was inhibited in animal models of B16-F10 melanoma and CT26 colon cancer when mRNA vaccines encoding these neoepitopes were administered . Some reOn occasion, the breaching of the cell membrane and the enhancement of the efficient uptake of mRNA in vivo have been accomplished through the use of physical methods . In ordeIn spite of the fact that protamine, a cationic peptide, has been demonstrated to protect mRNA from degradation by serum RNases, protamine-complexed mRNA alone demonstrated limited protein expression and efficacy in a cancer vaccine model . This coAlthough there are commercially available highly efficient mRNA transfection reagents that are based on cationic lipids or polymers and work effectively in a large number of primary cells and cancer cell lines, these reagents frequently exhibit either limited efficacy in vivo or a high level of toxicity . TransITOne strategy to vary the amount of in vivo protein synthesis as well as its duration is by changing the route through which mRNA-LNP vaccines are delivered to the body . In the Cancer vaccines based on messenger RNA have been the subject of recent, in-depth last investigations . Figure Cancer vaccines and other forms of immunotherapy represent promising new approaches in the war against the disease . Tumor-aTumor cell-based vaccines are a type of cancer vaccine that involves using whole tumor cells to stimulate an immune response against cancer . These vAutologous Tumor Cell-Based Vaccines: Autologous tumor cell-based vaccines are personalized vaccines created from the patient's own tumor cells . A smallAllogeneic tumor cell-based vaccines are developed using tumor cells obtained from a donor or cell lines derived from tumor tissues. These cells are not specific to the patient receiving the vaccine . AllogenPeptide-based vaccines are a type of cancer vaccine that utilizes small protein fragments called peptides to trigger an immune response against cancer cells . These pPeptide-based vaccines are typically administered through injection, either subcutaneously or intramuscularly. Upon injection, the peptides are presented to immune cells called antigen-presenting cells (APCs) such as dendritic cells. APCs engulf the peptides and process them internally . The proCTLs recognize the MHC-peptide complexes as foreign or abnormal, indicating the presence of cancer cells displaying those specific antigens. This triggers the activation of CTLs, leading to their proliferation and the release of immune molecules such as cytokines and perforins . The actPeptide-based vaccines offer several advantages. They are highly specific because they target tumor-specific antigens, minimizing the risk of off-target effects. Peptides can be synthesized relatively easily and can be modified to enhance their stability and immunogenicity . AdditioViral vector-based vaccines are a type of vaccine that uses modified viruses as carriers or vectors to deliver specific antigens into the body . These vThe viral vectors used in these vaccines are often derived from naturally occurring viruses, such as adenoviruses or lentiviruses. These viruses have the ability to infect cells and deliver their genetic material . HoweverThe immune system recognizes the antigen as foreign and mounts an immune response against it. This response includes the activation of immune cells, such as T cells and B cells, which are essential for eliminating the targeted antigen . The immViral vector-based vaccines offer several advantages . They haNucleic acid-based vaccines represent a promising approach in the field of immunization, offering a unique strategy to induce protective immune responses against various diseases, including cancer . These vDNA-based vaccines are designed by inserting the gene encoding the desired antigen into a circular DNA plasmid. This plasmid is then administered directly into the body through intramuscular or intradermal injection . Once inRNA-based vaccines, on the other hand, directly utilize mRNA molecules encoding the desired antigens . These mThe use of DCs in cancer immunotherapy made sense because of their pivotal function in initiating antigen-specific immune responses . Some reImmune regulatory proteins can be found in abundance in mRNA-encoded adjuvants, which can increase the potency of DC cancer vaccines . MultiplElectroporation of antigen-encoding mRNA or mRNAs is possible in conjunction with the adjuvant cocktail TriMix, which includes mRNA-encoded CD70, CD40L, and constitutively active TLR4 . This foTrials using DC immunization have been conducted for a variety of cancers, including prostate, lung, brain, and pancreatic cancers . It is aThe manner in which messenger RNA vaccines are delivered can have a substantial impact on the degree to which they are successful . Figure In the antigen presentation process, APCs like DCs encounter or receive externally-loaded antigens before injection. These antigen-loaded APCs travel through the lymphatic system to draining lymph nodes, where T cell activation primarily occurs. Mature DCs present tumor-derived peptides on MHC class I and II molecules to CD8 + and CD4 + T cells, with costimulatory "signal 2" interactions enhancing the development of tumor-specific T cell responses. IL-12 and type I interferons produced by DCs further augment costimulation. These interactions result in the generation and expansion of activated tumor-specific CD4 + and CD8 + T cell populations, which can then migrate to the tumor site and destroy tumor cells.The lysed tumor cells release antigens that induce polyclonal T cell responses, increasing the antigenic variety of the anti-tumor immune response and leading to epitope spreading. Cancer vaccines are composed of four main components: tumor antigens, formulations, immune adjuvants, and delivery vehicles. These components work together to facilitate a robust and targeted immune response against cancer cells. In the process of developing mRNA cancer vaccines, both conventional and nonconventional administration systems  have been utilized . ConventNaked mRNA vaccines are easy to manufacture and administer, and are relatively low-cost. However, they suffer from inefficient delivery and translation, leading to low immunogenicity. These vaccines have moderate efficacy and good safety, but their stability is hindered by a short half-life and susceptibility to degradation by RNases. Naked mRNA vaccines require a high dosage and have low manufacturing complexity, with Phase I/II clinical trials serving as references. An unconventional technique of delivering vaccines that uses intranasal administration of naked mRNA has been shown to be successful . If mRNAIn mice, mRNAs encoding tumor-associated antigens were delivered intranasally using the TriMix adjuvant . This reDCs electroporated with mRNA encoding the melanoma-associated antigens tyrosinase or gp100 and TriMix and administered intranasally to patients with advanced melanoma evoked modest antitumor responses, according to the findings of one investigation . IntranaIn a more recent study, researchers found that intratumor delivery of mRNA encoding an engineered cytokine based on interferon- (IFN) fused to a transforming growth factor- (TGF) antagonist increased the cytolytic capacity of CD8 + T cells and modestly slowed the growth of tumors in OVA-expressing lymphoma or lung carcinoma mouse models . These fSystemic distribution of mRNA vaccines is unusual because of concerns over aggregation with serum proteins and rapid extracellular mRNA degradation ; as a reWhen numerous cationic LNP-based complexing agents are administered intravenously, the majority of the traffic is directed to the liver, which may not be in the best condition for DC activation . Recent A positively charged lipid particle's major target was the lung, while a negatively charged particle's primary target was DCs in secondary lymphoid organs and the bone marrow . The negmRNA is synthesized in vitro utilizing recombinant enzymes, ribonucleotide triphosphates (NTPs), and a DNA template . Due to To prevent mRNA instability, formulation buffers may include antioxidants and chelators . DevelopNeither the FDA nor the European Medicines Agency (EMA) have established any concrete rules concerning mRNA vaccine products as of yet . As can The mRNA vaccines have gained significant attention and recognition in recent years due to their remarkable potential in combating infectious diseases . They ofOne of the notable advantages of mRNA vaccines is their safety profile. Unlike traditional vaccines that may contain attenuated or inactivated pathogens, mRNA vaccines do not require the introduction of live organisms into the body . InsteadSeveral different mRNA vaccines have been shown to be safe and well tolerated after going through clinical studies ranging from phase I to phase IIb of testing . On the Inflammation can occur both locally and systemically . SeveralAdjuvants play a crucial role in enhancing the stability and efficacy of mRNA vaccines . mRNA vaAdjuvants are substances that are often used in vaccines to enhance the immune response generated by the vaccine . While tOne important aspect of their function is their ability to enhance cellular uptake, which refers to the process by which cells internalize and take up external substances or particles . AdjuvanAdjuvants are substances that are often recommended for use in mRNA vaccines to enhance their effectiveness by promoting immune activation . mRNA vaAdjuvants can influence the type and strength of the immune response elicited by the mRNA vaccine . DiffereAdjuvants can extend the presentation of the antigen  derived from the mRNA vaccine . They heThe mRNA vaccines are administered to individuals who are at risk of being exposed to a specific pathogen, such as a virus or bacteria , 312. ThThe mRNA vaccines have gained recognition for being largely risk-free and well-tolerated . There aFigure IVT mRNAs, which mimic the eukaryotic mRNA, usually have a N7-methylated guanosine added to the first 5′ nucleotide through a 5′, 5′-triphosphate bridge for efficient translation in the eukaryotic system . This 5′Besides the enzymatic post-translational capping methods, chemical capping methods add cap analogs co-transcriptionally . However5’cap can be added enzymatically after IVT to achieve 100% capping efficiency with a natural unmodified cap structure . HoweverXenopus laevis or humans contain translation and stability regulatory elements, and are commonly used as the 3′ UTR of IVT mRNA [UTRs can impact mRNA degradation rate and translation efficiency through interacting with RNA binding proteins . 5′ UTR IVT mRNA . To furtIVT mRNA . MoreoveIVT mRNA . OverallIVT mRNA . One neeIVT mRNA .Optimization of G and cytosine (C) content in the ORF can be applied to regulate the translation elongation rate . UridinePoly(A) sequence can slow down the degradation process of RNA exonuclease, increase RNA stability, and enhance translation efficiency . A suitaAnother method to improve mRNA stability, translation efficiency and mRNA vaccine potency is to modify mRNA transcripts with alternative nucleotides . Pseudou2+ concentration or by producing RNA at elevated temperature [As mentioned in Sect. "perature . A more perature . Howeverperature . Recentlperature . Anotherperature . For insperature . The RNAperature . Figure As mentioned earlier, type I IFN shows paradoxical impact on the immune response of mRNA cancer vaccine . Several+ T cells response [Some researchers have reported mRNA pulsed with a palmitic acid-modified TLR7/8 agonist R484 markedly improved the MHC class I presentation of OVA mRNA derived antigen in APCs, subsequently induced a more effective adaptive immune response in a tumor bearing mouse model as compared to mRNA vaccine without the adjuvant . Moreoveresponse . Improveresponse .+ T cell responses [Recently, a combination of pro-inflammatory cytokines and chemokines have also been exploited to boost the antitumor immunity of mRNA vaccines in both pre-clinical and clinical studies . In one esponses . The cytesponses . For insesponses . Studiesesponses .Innate immune response is usually activated by host immune system through detecting exogeneous motifs called PAMPs via the pattern recognition receptors (PRRs) . These rThese PRRs can sense different RNAs, including dsRNA and single stranded RNA (ssRNA), blocking mRNA translation as reviewed elsewhere . The act+ 8 T cell activation [+ T cell immunity have been extensively reviewed elsewhere. In brief, the stimulatory or inhibitory actions of type I IFNs on CD+ 8 T cell activation is likely to be dependent on the timing and kinetics between activation of IFNAR signaling and TCR signaling, which may be further impacted by the routes of administration of mRNA cancer vaccines [+ T cell response to systemic mRNA vaccination [The paradoxical impact of Type I IFNs activation is not only restricted to antigen expression, but also shown on CDtivation . The duavaccines . For inscination . One hypcination . In contcination . Howevercination . Therefocination . Figure Another RNA vaccine platform that holds the promise to maximize the magnitude and length of antigen production is SAM . SAMs arEarly investigation of SAM is direct injection of SAM packaged into viral replication particles (VRP) . VRPs arSince SAM is a huge and negatively charged molecule (~ 9500 nt), a delivery system is needed for its effective cellular uptake and protection from enzymatic degradation . Over thSome researchers presented a new vaccine platform based on self-amplifying RNA encapsulated in synthetic LNPs , 426. ThClinical applications of SAM (delivered by VRPs and LNPs) in the prevention of infectious disease are promising, which have been extensively reviewed elsewhere. However, the applications of SAM in cancer vaccine are mainly limited to preclinical studies, with only two clinical trials ongoing using VRP delivered antigens against colorectal cancers . The cliTable Various viral, non-viral, and cell-based vehicles have been developed to increase the delivery efficiency of mRNA cancer vaccines . Viral aLNPs are known for their high delivery efficiency and low toxicity, while cationic polymers offer low cost and customizability. Peptide-based delivery systems boast a high level of customization and potential for targeted delivery. Other methods, such as in vivo electroporation, physical delivery methods like laser microporation and sonoporation, and the use of gold nanoparticles, provide targeted delivery with varying levels of immunogenicity and efficacy. Biodegradable microspheres, cell-based vaccines, and protein and peptide-based systems have shown promising results in certain cancer targets, while electrospray and RNA-lipoplexes provide high antigenicity and low toxicity, respectively. Novel methods like mRNA-coated gold nanorods and nanocarriers offer targeted delivery and reduced toxicity. Collectively, these delivery systems offer unique advantages and challenges in the fight against cancer, with their success largely dependent on the specific cancer target and delivery method employed.Ex vivo DC mRNA allows for the loading of DCs with tumor-specific antigens but involves complex and costly procedures. In vivo naked mRNA is simple and easy to administer but has limited efficacy due to degradation in the body. Lipid nanoparticles and polymer-based delivery systems offer high efficiency of mRNA delivery, with the former being more efficient. Peptide-based delivery offers highly specific targeting of cancer cells, but limited clinical data and validation are available. Self-amplifying mRNA (SAM) requires lower doses for efficacy, while in vivo electroporation increases cellular uptake, reducing the need for delivery vehicles. Jet injection and hydrodynamic delivery are simple and easy to administer, but their efficacy is limited due to mRNA degradation. Physical delivery methods can achieve high mRNA delivery efficiency but may cause tissue damage or be limited by tissue barriers. In vitro transcription allows for large-scale production of mRNA but requires high cost and complex procedures. The safety and stability of these delivery systems are generally considered acceptable, but there are variations in efficacy and immunogenicity among them. Table Ionizable lipid nanoparticles (LNPs) are a type of lipid-based delivery system used for the delivery of mRNA in mRNA vaccines. They are composed of a lipid bilayer that encapsulates the mRNA payload, protecting it from degradation and facilitating its delivery into target cells. The term "ionizable" refers to the presence of ionizable lipid components within the nanoparticle structure. The lipid component forms the core structure of the nanoparticle and provides stability. It is usually a mixture of different lipids, including ionizable cationic lipids, neutral lipids, and cholesterol. The ionizable cationic lipids play a crucial role in endosomal escape and efficient intracellular delivery of mRNA.The mRNA encoding the desired antigen or therapeutic protein is encapsulated within the lipid bilayer of the nanoparticle. The mRNA is synthesized in vitro and then complexed with the lipid components to form the LNP. Polyethylene glycol (PEG) is often included in the LNP formulation to provide stability and prevent aggregation of nanoparticles. PEGylation also helps to prolong the circulation time of LNPs in the body, enhancing their chances of reaching the target cells. In some cases, LNPs can be modified with targeting ligands on their surface to facilitate specific binding to target cells or tissues. This modification enhances the specificity and efficiency of LNP delivery to the desired cells, improving vaccine efficacy.The ionizable LNPs exploit the natural process of endocytosis, where cells engulf extracellular materials, to deliver the mRNA payload into the target cells. After the LNPs are taken up by cells, they are internalized into endosomes. The ionizable cationic lipids within the LNPs interact with the negatively charged endosomal membrane, leading to disruption of the endosomal structure and release of the mRNA payload into the cytoplasm. Once inside the cytoplasm, the mRNA is available for translation by the cellular machinery to produce the desired protein or antigen. The protein or antigen is then presented to the immune system, triggering an immune response and the production of specific antibodies or activated T cells. Ionizable lipid nanoparticles have gained significant attention in the field of mRNA vaccines due to their ability to efficiently deliver mRNA into cells, resulting in high protein expression levels and potent immune responses. They have been successfully used in the development of mRNA-based COVID-19 vaccines, such as the Pfizer-BioNTech and Moderna vaccines. The versatility and effectiveness of ionizable LNPs make them a promising tool for the delivery of mRNA-based therapeutics and cancer vaccines.There are various types of lipid nanoparticles employed for mRNA vaccine delivery, each with their unique advantages and disadvantages. PEGylated lipids offer increased circulation time and reduced toxicity but suffer from poor transfection efficiency and manufacturing challenges. Cationic lipids boast good transfection efficiency and easy manufacturing but can be toxic and unstable. Neutral lipids provide high stability and low toxicity, while pH-sensitive lipids enable endosomal escape in acidic environments, but both have limited transfection efficiency. Ionizable lipids have high transfection efficiency and stability but are also associated with toxicity and off-target effects. Multi-component lipids, pro-nano liposomes, dual-function polymer-lipid nanoparticles, and targeted lipid nanoparticles offer enhanced stability, reduced toxicity, and improved transfection efficiency, albeit at the cost of complex manufacturing processes and potential off-target effects. Researchers continue to explore and develop these lipid nanoparticles to optimize the delivery of mRNA vaccines, aiming to maximize efficacy, safety, and stability while minimizing disadvantages and off-target effects.II phases, resulting in fusion and disintegration of the endosomal membrane [2-carbon of the ester group helps the lipid maintain the “cone shape” to destabilize the endosomal membrane [LNPs are a type of delivery system that encapsulate the mRNA in a protective lipid coat to improve its stability and delivery efficiency Table . LNPs, wmembrane . Multiplmembrane . Linoleimembrane . For insmembrane . The linmembrane . Increasmembrane . Howevermembrane . Replacimembrane . Finallymembrane . Some remembrane . Structumembrane . Howevermembrane . Unfortumembrane . Althougmembrane . A persimembrane . Ester lmembrane . Cleavagmembrane . In the membrane . Moreovemembrane . For insmembrane . Howevermembrane . A balanmembrane . Subtle membrane . Replacimembrane . In somemembrane . For examembrane . Therefomembrane . In addimembrane . Some remembrane . The resmembrane . The sammembrane . The higmembrane . Some remembrane . Organ smembrane . For insmembrane . Based omembrane .Table 8TThe rationales and mechanisms behind LNP internalization, endosomal escape and organ/cell-selective delivery have been widely investigated by multiple groups using either siRNA or mRNA as the delivered molecules . In brieInsufficient release of mRNA/siRNA from endosomal compartment has been considered as the predominant obstacle that limits the expression of mRNA/siRNA delivered by LNPs . IntraceThe positive effect of lipid nanoparticle (LNP) formulation on protein production in vivo following intramuscular administration is demonstrated. Figure The experiment involved BALB/c mice, divided into two groups of four. One group received intramuscular injections of non-formulated mRNA (mRNA), while the other group received LNP-formulated mRNA (mRNA/LNP) containing the PpLuc mRNA. Two time points, 24- and 48-h post-injection, were analyzed for luciferase expression using in vivo optical imaging (a). The luminescence was then quantified to measure the luciferase expression (b). The data points for individual mice are represented as dots, while the median for each group is shown as a solid line. Figure Immunogenicity of the delivery materials were also evaluated and applied to boost immune response of the cancer vaccines . Some reFigure Polyamines, dendrimers, biodegradable copolymers are commonly used polymer-based materials for mRNA delivery . PolymerPolymer-based mRNA delivery systems exhibit a range of characteristics, each with its own advantages and disadvantages. Polyethyleneimine (PEI) has high transfection efficiency but is cytotoxic at high doses. Poly(lactic-co-glycolic acid) (PLGA) is biodegradable and biocompatible but has limited transfection efficiency. Chitosan offers good biocompatibility and biodegradability but similarly suffers from limited transfection efficiency. Polyethyleneglycol (PEG) is non-immunogenic and biocompatible, though it has poor transfection efficiency. Various other polymers such as poly(amidoamine) (PAA), poly(beta-amino esters) (PBAEs), and poly-L-lysine (PLL) also have their unique sets of advantages and drawbacks, with some exhibiting high transfection efficiency but potential cytotoxicity or immunogenicity, while others have biodegradable and biocompatible properties but limited transfection efficiency or stability. The choice of polymer-based mRNA delivery system depends on the specific needs and requirements of the application, balancing factors such as immunogenicity, efficacy, safety, stability, and mechanism of action.Polyethyleneimine (PEI) is one type of cationic polymer commonly used for nucleic acid delivery . The comThe cationic peptide, protamine has been used in many early studies for the delivery of mRNA vaccines . ProtamiCationic cell-penetrating peptides (CPPs) can complex with RNA . AlthougIn additional to ionizable lipid composed LNP system, cationic lipid composed liposomes, lipoplexes and cationic emulsions (CNE) are the very first generation of carriers used for mRNA delivery both preclinically and in clinical trials . DOTMA  and subcutaneous (SC) injections are simple to administer and induce a systemic immune response, but they may require multiple doses and have limited local response. Both methods show moderate to high immunogenicity, efficacy in some cancers, and general safety with mild side effects. Intradermal (ID) injections induce a strong local and systemic immune response with potential for dose-sparing, although they may require specialized equipment. Intravenous (IV) injections can also induce systemic immune response, but they require high dosages and specialized equipment, which may lead to non-specific immune responses. Lastly, intra-tumoral (IT) injections allow direct delivery to the tumor site and have the potential for targeted response, but may also require specialized equipment and offer limited systemic immune response. All injection routes, except for intravenous, are stable at refrigeration temperature and need protection from light, while intravenous injections are stable at ultra-low temperatures and have a short half-life in circulation.+ T cell response and circumvent the detrimental impact of mRNA inherent innate immunity [Intramuscular administration is often preferred due to the flexibility of injection volume, the ease of dosing and the lack of safety concern, with limited risk for adverse reactions at the site of injection, However, vaccine delivered to the skin as a highly immunocompetent site has long been considered a strategy to augment vaccine response . Some reimmunity . As a reimmunity . Howeverimmunity . Until nimmunity .Transfection of mRNA into DCs for adoptive transfer was the first mRNA based therapeutic cancer vaccine entering clinical trial . AlthougA group of IVT mRNA-based immunotherapies investigated in clinical trials are mRNAs encoding immunostimulants which are injected intratumorally or intranodal to modify the suppressive tumor microenvironment . These iMultiple IVT mRNA-based cancer vaccines are currently tested in clinical trials, either encoding personalized neoantigens, or a cocktail of TAAs . DeliverImmunostimulants are commonly cytokines or chemokines that induce APC maturation and activation, activate T-cell mediated immunity and adjust the dysfunctional immune tumor microenvironment . Intra-tFigure + T cells, the activation stimulator CD40 ligand (CD40L) to activate CD4+ T cells, and the constitutively active TLR4 (caTLR4) to facilitate DC antigen presentation [The company has developed a TriMix mRNA-based adjuvant that consists of three naked mRNA molecules, encoding the costimulatory molecule CD70 to induce activation of CD8entation . The nakentation . Deliverentation . In two entation . Anotherentation . These tentation . One proentation . The othentation . In mRNAentation . IL-36Ƴ entation . IL-36 Ƴentation . IL-23  . The nakresponse . The preresponse . Table 1Shared TAAs target a broad range of tumors, offering potential for cancer prevention, but exhibit low specificity and efficacy against certain cancers. Differentiated TAAs provide high specificity against individual cancer cells but are only effective against specific types of cancer cells. Cancer-testis antigens are specific to tumor cells and not found in normal adult tissues, although their expression may vary between patients. Overexpressed TAAs are present in a wide range of cancers but can also be expressed in normal tissues, leading to potential toxicity. In general, immunogenicity ranges from moderate to high, with efficacy varying depending on the TAA and cancer type. While these mRNA vaccines are generally considered safe, they may cause autoimmune reactions. All TAA types have a short half-life, which necessitates frequent dosing.According to Table The utilization of TAAs in mRNA cancer vaccines has the potential to revolutionize cancer treatment and provide new therapeutic options for patients. These antigens can be targeted by the immune system to eliminate cancer cells. Several obstacles limit the further application of TAA vaccines, including: (1) only limited TAAs have been identified for certain solid tumors resulting in limits of applications, and (2) patients harboring extensive variability in TAAs that gives rise to evasion of immune effectors and generation of resistance, (3) TAAs are also present in normal tissues . VaccineTumor-specific antigens, termed neoantigens, are now the core targets of mRNA vaccines. Neoantigens are derived from random somatic mutations in tumor cells and not present in normal cells . Neoanti+ T cells and CD8+ T cells response [Various types of variant mutations can be targeted by neoantigen based vaccine . Multiplresponse . Four ofresponse . Howeverresponse .Most recently, some researchers reported that immunizing advanced melanoma patients in a clinical study using IVT mRNA encoding neoantigens through intranodal (i.n.) injection . The ultII molecule. Vaccines targeting these exogenous antigens induce neutralizing antibodies mediated humoral response. In some cases, CD4+ T cell-mediated immune response is partially involved and required, whereas CD8+ cytotoxic T cells play crucial roles in the clearance of malignant cells with somatic mutations. Thus, the anticancer therapeutic vaccine not only needs to boost humoral response, CD4 + T cell response but also needs to activate the MHCI mediated CD+ 8 T cells responses, which further adds to the difficulties for efficient boosting of a robust antitumor immunity. Another major hurdle for efficient anticancer vaccine development is to identify and efficiently deliver highly immunogenic tumor-specific antigens. Tumor antigens are highly variable across different individuals, and some are less immunogenic and can invade the recognition by the host immune system. Even if the antigen is immunogenic, a suppressive microenvironment could prevent effective T cells’ infiltration and cause T cell exhaustion. Lastly, as a therapeutic vaccine for treating a chronic disease like cancer, multiple/repeatable dosing with higher dosage than prophylactic vaccines is required, raising the safety criteria for both mRNAs and the carriers. Among other cancer vaccines, including DC-based vaccines and protein-based vaccines, mRNA stands out for several reasons: (1) mRNA could simultaneously encode multiple antigens, or a full protein with both MHCI and MHCII binding epitopes to facilitate both humoral and cellular adaptive immune response, providing a more intensified anti-tumor immunity. (2) Compared with DNA vaccine, mRNA vaccines are non-integrating, highly degradable, with no insertional mutagenesis potentials. Compared to protein or cell-mediated vaccines, the IVT production of mRNA is free of cellular and pathogenic viral components, with no infectious possibilities. Most mRNA vaccines tested in ongoing clinical trials are generally well tolerated, with rare cases of injection site reactions. Systemic inflammation may be a major concern for mRNA vaccines due to its intrinsic immunostimulant-like function to activate the TLR7/8 pathway and to induce the type I IFN responses. However, type I IFN mediated innate immune response could be reduced by removal of the dsRNA contaminants, codon optimizations, and nucleotide modifications. The innate immune response could also be restricted to the local injection site by properly designing the delivery systems and changing the administration routes. The activation of type I interferon responses is not only associated with inflammation but also potentially with autoimmunity. Therefore, identifying individuals at an increased risk of autoimmune reactions before mRNA vaccination is another precautious step necessary to be taken. (3) Another advantage of mRNA cancer vaccine is the rapid and scalable manufacturing. The mature manufacturing process of mRNA and formulation platform allows productions of a same or a new type of vaccine within a very short period. Although identifying immunogenic TAAs/TSAs and overcoming suppressive tumor microenvironment still remain major hurdles for mRNA vaccine, the recent discovery and identification of neoantigens facilitate personalized vaccine treatment applications. mRNA encoded neoantigens have become the frontrunner in the personalized vaccine campaign. Multiple clinical studies led by the mRNA LNP pioneers BioNTech and Moderna, already presented promising results (with a readout of antitumor immunity) using personalized vaccines in several clinical trials treating multiple solid tumors, including metastatic melanoma and aggressive pancreatic cancers, opening a new era for therapeutic cancer vaccines.With the recent approval of two mRNA LNP vaccines to prevent COVID-19, mRNA vaccines are experiencing a considerable burst in preclinical and clinical research in both cancer and infectious disease fields. The challenges of developing cancer vaccines versus infectious disease vaccines lie in: firstly, most infectious disease vaccines are prophylactic, whereas cancer vaccines are therapeutic. The cases for preventive cancer vaccines are rare with only two FDA approved such vaccines, and these two vaccines are applied to prevent virus-induced malignancies (HPV and HBV). Though anti-cancer prophylactic vaccines are still under pre-clinical investigation, the clinical translation is limited by the difficulties of antigen predictions and the suboptimal immunogenicity. Secondly, most antigens for infectious disease  are exogeneous motifs typically presented by the MHCTo further improve the potency of mRNA anticancer vaccines, multiple clinical trials are ongoing to evaluate the combination of mRNA vaccines with either cytokine therapies or checkpoint inhibitor therapies. mRNA is a powerful and versatile cancer vaccine platform. Its successful development towards clinical translation will remarkably strengthen our ability to combat cancers. Future investigations should continue focusing on (but not limited to) understanding and utilizing the paradoxical inherent innate immunity of mRNA, improving the efficiency of antigen expression and presentation by designing advanced and tolerable delivery systems, and modifying mRNA structures to achieve extended and controlled duration of expression.


Elevated protein levels in cerebrospinal fluid (CSF) can occur in various pathologies and are sometimes difficult to interpret. We report a 62-year-old male patient with subacute neurological deterioration, progressive tetraparesis, and cytoalbumin dissociation in the lumbar CSF. The patient had a pre-existing cervical spinal stenosis with mild tetraparesis. Based on the initial cytoalbumin dissociation , Guillain–Barré syndrome was initially considered. For further diagnosis, a CSF sample was taken from a pre-existing ventriculoperitoneal shunt, which showed a normal protein and cell count considering the patient’s age . In conclusion, we suggest that intermediate aggravation of tetraparesis was due to pneumonia with septic constellation, and the cytoalbumin dissociation was interpreted as Froin’s syndrome (FS) due to spinal stenosis. In this unique case, we were able to prove the -often suspected- case of FS by parallel analysis of ventriculoperitoneal shunt and lumbar CSF. The triad of xanthochromia, high protein levels, and marked coagulation was first described by Georges Froin and occurs in various processes leading to severe spinal stenosis. The altered composition of lumbar CSF might be due to impaired CSF circulation; however, the exact mechanisms of this phenomenon require further investigation. Cerebrospinal fluid (CSF) analysis is an important diagnostic tool in the workup of neurological diseases. An elevation of CSF protein level may indicate intrathecal protein synthesis or may occur in the context of a disrupted blood–brain barrier (BBB) during infections, leptomeningeal metastasis, or subarachnoid hemorrhage. As an alternative cause for increased CSF protein levels, disturbances in CSF circulation between the upper and lower spinal cord can falsify the CSF results from lumbar punctures. Highly elevated CSF protein levels in lumbar puncture should lead to the differential diagnosis of Froin’s syndrome (FS) during routine clinical practice . AlthougWe here report on a 62-year-old male patient who presented with increasing subacute neurological deterioration of a known tetra-paresis and impoverishment of speech production within one week. A cervical spinal canal stenosis  and supportive OAfter pulmonary stabilization, the patient was transferred to the neurological immediate care (IMC) for further diagnostics and treatment of the progressive tetra-paresis and vigilance mitigation.On admission, the clinical–neurological examination revealed a somnolent patient in reduced general condition, oriented to person only, without meningism. Cranial nerves were unremarkable, and no sensory disturbances were notable. A pronounced tetra-paresis with an emphasis on the left side and especially lower extremities, as well as generalized areflexia, were detectable. Disturbances of bladder and bowel function were not evident.Cranial imaging with computer tomography and magnetic resonance imaging (MRI) showed no evidence of acute ischaemia, hemorrhage, or elevated intracranial pressure. Shunt dysfunction was excluded by careful assessment of ventricular size, shunt location, and puncture by the neuroradiologists and neurosurgeons. We also compared CSF parameters of the ventriculoperitoneal shunt before the actual clinical deterioration from a previous clinical stay and did not observe significantly different parameters compared to the current analysis, also indicating stable shunt function and location. The pre-existing right frontal parenchymal defect and pituitary adenoma were unchanged from previous imaging.An additional MRI of the spinal cord showed myelopathy in the T2-weighted images at the level of cervical vertebra 3 and a narrowed spinal canal (Kang grade 3) constant to the previous findings 5 years ago see A–D and sIn the context of clinically worsened tetraparesis and cytoalbumin dissociation in the CSF, the diagnosis of Guillain–Barré syndrome was first considered, and intravenous immunoglobulins started. Additional neurographies showed conspicuous F-waves and mild amplitude reductions, which had already been described in a previous neurological work-up 4 years ago. The supplementary nerve sonography showed some caliber irregularities, but there was a regular presentation of the roots C5 on both sides and C6 left side, so additional diagnostics were not clearly supportive for GBS.For further clarification of the cytoalbumin dissociation, cerebrospinal fluid was taken from the ventriculoperitoneal shunt 3 days after the lumbar puncture. The findings were unremarkable , neuroimmunological diseases such as GBS, malignancies, intracranial hemorrhage, endocrinological diseases like hypothyroidism , degenerFS was first described by Georges Froin 1874–1932) in 1903 as a triad of high protein content, xanthochromia, and marked coagulation 874–1932  and assoIn conclusion, CSF protein elevations are a frequent finding, which can be attributed to various causes and are sometimes difficult to interpret in the clinical setting. Our case clearly demonstrates marked differences in the analysis of ventricular versus lumbar-derived CSF and highlights the complex mechanisms of CSF circulation. Further studies on the exact dynamics of both ventricular and lumbar-derived CSF, including pressure measurements, could contribute to a better pathophysiological understanding of CSF circulation.


Chemical toxicityevaluations for drugs, consumer products, andenvironmental chemicals have a critical impact on human health. Traditionalanimal models to evaluate chemical toxicity are expensive, time-consuming,and often fail to detect toxicants in humans. Computational toxicologyis a promising alternative approach that utilizes machine learning(ML) and deep learning (DL) techniques to predict the toxicity potentialsof chemicals. Although the applications of ML- and DL-based computationalmodels in chemical toxicity predictions are attractive, many toxicitymodels are “black boxes” in nature and difficult tointerpret by toxicologists, which hampers the chemical risk assessmentsusing these models. The recent progress of interpretable ML (IML)in the computer science field meets this urgent need to unveil theunderlying toxicity mechanisms and elucidate the domain knowledgeof toxicity models. In this review, we focused on the applicationsof IML in computational toxicology, including toxicity feature data,model interpretation methods, use of knowledge base frameworks inIML development, and recent applications. The challenges and futuredirections of IML modeling in toxicology are also discussed. We hopethis review can encourage efforts in developing interpretable modelswith new IML algorithms that can assist new chemical assessments byillustrating toxicity mechanisms in humans. The rapidly increasing numberof chemicals in medical, industrial, and agricultural fields has madeit impractical to use animal models for evaluating tens of thousandsof new chemicals.7 As an alternative strategy, computationaltoxicology using machine learning (ML) techniques has shown promisefor chemical toxicity evaluations because it can quickly predict thetoxicity of a large number of new compounds in the risk assessmentprocess and prioritize potentially hazardous compounds for experimentaltesting.8 In the National Research Council(NRC) 2007 report Toxicity Testing in the 21st Century: AVison and a Strategy, the development of computational techniquesfor risk assessment was emphasized.10 In 2016, theFrank R. Lautenberg Chemical Safety for the 21st Century Act (LCSA)was approved to advance chemical risk assessment. The LCSA calledfor computational approaches and strategies for safety evaluationto reduce or replace the use of vertebrate animals while providingevidence to support regulatory decisions.11 In chemical industries , the use of computationalmodels for chemical toxicity assessments is also important for decision-makingduring product development.12Chemical risk assessmentsand safety testing are important forthe early identification of hazardous chemicals in multiple industrysectors.9 With the development of advancedML and deep learning (DL) algorithms, computational modeling can usemassive toxicity data for more accurate chemical toxicity predictions.Some DL models have been shown to match or even outperform other MLalgorithms on prediction accuracy.16 However, a common limitationof complex ML models, especially DL models using neural network architecture,is their “black box” nature, which means their innerworking mechanisms cannot be easily understood by users.17 There is an increasing demand for developingstrategies to help toxicologists understand the model and how thepredictions are made. The development of interpretable ML (IML) isan effective approach to mitigate the lack of interpretability underlyinga trained model to reveal underlying toxicity mechanisms and to augmentdecision-making.Inthe past decade, the development of new experimental protocols,especially high-throughput screening (HTS) assays, and the progressof combinatorial chemistry has generated toxicity data for millionsof compounds.20 and health-related fields.23 However, without an understanding of working mechanisms, black boxmodels can lead to mistrust of the results.24 In health-related fields, black box models can negatively affecthuman health, racial bias, and safety.28 For example, Obermeyer et al.uncovered a racial bias issue in a widely used model for predictinghealth needs.26 Some pollution models incorrectlypredicted highly polluted air as nonhazardous to humans, due to theunknown working mechanism of the models.27 Such negative consequences caused by black-box ML models can beavoided by developing IML with increased transparency and interpretability.30 Lundberg et al. reported the use of IML for the prevention of hypoxemiaduring surgery, which can provide explanations of the risk factorsin real-time and increase the anesthesiologists’ anticipationof hypoxemia events by 15%.31 Recent applicationsof IML in the healthcare field showed IML’s potential in detectingbias and ensuring the interpretability of the model while maintainingaccuracy.35ML models are being ubiquitously used in dailylives 39 Ideally, besides the predictions made by a model,the knowledge about chemical toxicants in the training data can helptoxicologists better evaluate the trained model and make decisionson new compounds, i.e., determining which environmental chemicalsand drugs are of the greatest potential concern to human health.40 Because different scientific communities useML for different prediction tasks, there is no universal definitionof IML.36 Regarding the chemical toxicityassessments, the desired IML models need to fulfill the followingcriteria:Models should be constructedby explicit/understandablearchitectures.Users can understand howthe model reached a specificprediction.Models can provide toxicityknowledge insights to supportdecision-making.IML can perform robust model validations to avoid making wrongdecisions learned from biased training data and make the underlyingdecision-making understandable.Recent efforts to developIML and facilitate its application intoxicology are discussed in this review. We first overview featuredata of chemicals that can be used for toxicity model development.Then, examples of computational algorithms and their specific interpretationmethods are presented, followed by strategies for explaining blackbox models. The use of biological and toxicological knowledge in guidingthe design of IML models is then discussed. Finally, we conclude withpotential challenges in the practical applications of IML in toxicology.242 A typical procedure to develop a predictive model includes datacollection and curation, model building, and model validation. Differenttypes of data present different features and different levels of interpretabilityfor users.36 Unlike raw data (such as pixelunits in an image),29 most of the dataused in toxicity modeling reflect the properties/activities of chemicals,which possess the ability to be interpreted during the modeling processand/or after the model has been developed. Therefore, the trainingdata consisting of different feature types in toxicological modelingis the base of IML.MLis the technique to build predictive models by learning frominput feature data using computational algorithms.2.136 Quantitative structure–activityrelationship (QSAR), a statistical approach that correlates a compound’schemical structural or physicochemical properties to its activities,has been used traditionally for chemical toxicity modeling.43 The molecule structures were normally transferredinto molecular descriptors at the beginning of a modeling procedure.The calculated molecular descriptors can represent local or globalsalient characteristics of the structures  and InChI  and connection table-based file formats suchas SDF (Structure Data Format).47 These chemical structuredata can be accessed from chemical data-sharing repositories, suchas PubChem and ChEMBL  to generate descriptors for toxicologicalmodeling.Fortoxicity modeling, the most intuitive data to be used is the chemicalstructure information. To make it machine-readable during modeling,chemical structures need to be transformed into vectors of numericalor binary values.ructures 1. Major ructures 1A represructures 1B; (c) cructures 1A; (d) gructures 1E; and . Structureannotation techniques, such as Delaunay tessellation, which decomposesthe surface of nanostructures into tetrahedra, have been developedto generate nanodescriptors that simulate surface chemistry and propertiesof complex nanoparticle structures , theuse of only structural information and chemical properties for modeling is error-prone, particularly when compounds with similarstructures or chemical properties exhibit dissimilar toxicities.5Besides structural/chemical descriptors,chemical molecules canbe treated as graphs, where graph embedding techniques are appliedto generate feature vectors for modeling. During the graph embeddingprocess, chemical molecules are first represented as undirected graphswith atoms as nodes and edges as bonds 1D, and tections” 1E, whichructures 1F.52,53 2.2in vitro biomolecular or cell-based assays, thereby resulting in the HTStechnique capable of screening thousands to millions of compounds.58 The adverse outcome pathway (AOP) is a conceptual framework thatlinks chemical-induced responses at the molecular, cellular, and organlevels to adverse outcomes at the organism level. Mechanism-basedassay outcomes can be used within an AOP pathway to systematicallyassess whether a compound is likely to induce the target adverse outcome.59 The chemical responses obtained from target-specific, mechanism-oriented in vitro assays in HTS projects like ToxCast and Tox2163 keep growing and have contributed to the current toxicity big data or lowest effective concentration (LEC), can beused for the extrapolation of in vivo equivalentdose and prediction of toxicity potentials.68 In parallel with the progress of various HTS projects, several data-sharingprojects were also developed in the past decade  database. GEO is aninternational public repository that archives and freely distributesmicroarray, next generation sequencing, and other forms of high-throughputfunctional genomics data submitted by the research community.83 In addition to gene expression data, some databases also provideassociations between chemicals, protein/gene targets, and diseasethat can aid in the mechanistic modeling of chemical-induced adverseoutcomes. For example, Comparative Toxicogenomics Database (CTD) isa publicly available database of manually curated toxicogenomic informationextracted from literature  also accelerate the application of gene-expression modelingto understand the toxicity mechanism of toxicants.terature 2.84 It p391 and can be achieved before and after modeltraining .97 Decision rules are also interpretable models that follow a generalIF–THEN structure: IF the conditions are met, THEN the modelmakes a certain prediction. The conditions are built from interpretablefeatures where pairs of conditions can be combined with AND/OR.91 Decision trees are graphs to represent multiple true/false questionsin a tree structure, where internal nodes represent tests on featuresto split the samples, edges represent the split decisions, and leafnodes represent corresponding class labels  is an algorithm that aims to find the hyperplanethat best separates samples, such as chemicals, by their labels whenthe samples are placed in a high-dimensional feature space (100ML includesthree basic branches: supervised learning that aims to learn a mappingfrom features to labels (toxicity) on the basis of labeled trainingsamples, unsupervised learning that aims to find patterns from unlabeledsamples, and semisupervised learning that combines labeled sampleswith unlabeled samples during training.s labels 3A.93 Prere space 3B. SVM c90 Besides that, they are also interpreted on the modular level aftermodel training, i.e., understanding the effects of training data andmodel parameters on predictions. For example, the weights of a linearmodel can be described as reflecting the strengths of relationshipsbetween features and target toxicity.17 The positive weight of a feature means this feature contributesby increasing the model’s output and vice versa.36 In a decision tree model, the importance ofa feature can be computed by going through the splits where the featurewas used and measuring the increase of accuracy compared with theparent node.91 By extracting the coefficientweights that define the hyperplane in a linear SVM model, toxicologistscan interpret the features (molecular patterns) that were assigneda higher absolute weight as having stronger impacts on toxicity prediction.102 The interpretation of nonlinear SVM is relatively complex and shouldbe based on the specific kernel used for transforming the data.104Most of the above algorithms are interpretable since theirstructuresand inner working mechanisms are transparent.36 For example, a decision tree can be hard to interpret ifit has a large width and depth. As an ensemble learning approach,random forest (RF) combines many decision trees to generate one prediction was used to identify importantfeatures, and feature interaction networks were built to explore theinteractions among multiple features. The modeling approach selectionshould depend on the complexity of the problem, specifically the relationshipbetween input features and target toxicity labels. Linear regressionor linear SVM models will not be applicable when the relationshipbetween input features and toxicity is not straightforward. Duringthe interpretation of feature importance, correlated features maycause issues where weights are split between them and feature importanceis underestimated. A possible solution is to apply feature selectionto remove redundant or irrelevant features, which can reduce the model’scomplexity and increase interpretability.112It should be noted thatwhen the size and complexity of a ML modelincrease, the model will become less interpretable.ediction3D and is3.2115 The structure of an ANN mimicsthe interlinked neurons in the brain, where a set of input nodes connectsto a second set of nodes called the “hidden” layer andthen eventually to an output layer.116 Aweight is associated with each of these connections between nodes,and there may be more than one hidden layer to construct a “deep”neural network (DNN) , two DL models are trainedduring contesting with each other, the generative network generatessynthetic data, and the discriminative network distinguishes syntheticdata from true data distributions (119 A recent study developed a GAN-based modelingframework (Tox-GAN) that learned from existing animal transcriptomicprofiles to generate new transcriptomic profiles on the basis of chemicalstructures, doses, and treatment durations.73 Tox-GAN can generate transcriptomic profiles without animal testing,which facilitates an understanding of toxicity mechanisms of new compoundsand enhances the biomarker development in predictive toxicology.The advancement ofcomputational infrastructure stimulated the applications of advancedML algorithms to address the challenge of the explosive growth oftoxicity data. DL is a part of the ML family based on artificial neuralnetworks (ANN) with representation learning. DL algorithms are beingapplied widely in fields, including voice and image recognition, languageprocessing, and clinic studies, because of their good predictive performancein modeling complex systems.rk (DNN) 3G. Otherrk (DNN) 3H which ibutions 3I, which91 This process may involve millions of weightparameters depending on the architecture of the DNN, thereby makingit difficult to understand the meanings of inner neurons and weightsand how the predictions are made. To resolve this issue, methods forinterpreting DNN models have been developed and can be classifiedas three major categories. Backpropagation-based methods calculatethe gradient of an output with respect to the inputs to derive thecontributions of features.123 Connection weight-based methods track the magnitudes and directionsof weights between neurons to identify individual and interactingeffects of input variables on the outputs. It enables the estimationsof feature importance when summing all connection weights.124 Investigation of neuron representations looksat the hidden neuron representations to provide explanations. Forexample, in CNN, visualization of the inner neurons' output canshowthe encoded meanings of the original image.126 In a DNN model for toxicityprediction using chemical structure data, Mayr et al. visualized thefragments represented in hidden neurons of different layers and foundhigh correlations between neuron representations and toxicophores.15 This study shows that new chemical knowledgecan be found from the hidden neurons of DNN. Backpropagation-basedmethods have also been used in DL toxicity models to extract importantsubstructures for toxicity prediction and further identify potentialtoxicophores.128 Some software tools have beendeveloped to facilitate DL interpretations. For example, Lucid  is an open-source toolbox containing methodsfor visualizing and interpreting neurons in ANN. iNNvestigate is acomprehensive library for implementing multiple interpretation methodsfor ANN models.129Since the internal structures and underlying working mechanisms areless interpretable, DNN models are black-box in nature compared withclassic ML models. To make predictions with a DNN model, input datapass through many layers of multiplications with the learned weightsand through transformations by activation functions that can be nonlinear.3.3130 biological assays,132 and chemicals133 in groups to help mechanisticinterpretations and predictions of chemical toxicities. On the basisof the hypothesis that similar chemical share similar toxicologicalprofiles, the read-across strategy was developed to predict toxicityfor new compounds using similar compounds with known toxicity results,which is easy to interpret and implement.134 Traditional read-across studies are only based on chemical structuresimilarity.137 Software tools like ToxMatch and OECD QSARToolbox use chemical structure-based similarity to perform chemicalgrouping and read-across.139 Similar to QSAR, onlyusing structural information is error-prone when compounds with similarstructures exhibit dissimilar toxicities.55 To address this issue, a hybrid read-across strategy was developedby combining chemical structure similarity and biosimilarity, whichis calculated on the basis of biological profiles .140 The hybrid read-across couldimprove the discriminational power to distinguish compounds with similarchemical structures and could reveal the potential toxicity mechanismsby examining the bioprofiles.141Besides thesupervised learning algorithms discussed above, unsupervised techniques,such as clustering and matrix factorization algorithms, have beenapplied to study the feature variable relationships and reveal novelpatterns. Clustering is the task of grouping a set of objects so thatobjects in the same cluster are more similar to each other than thosein other clusters 3C. Clust142 It works by decomposing a high-dimensional matrix into a productof two low-dimensional matrices to capture key patterns in the data (72Matrix factorization (MF) is a collaborative filtering algorithmcommonly used in recommendation systems.the data3E. For eis (GFA) 3F, was a490Besides interpreting ML models on the basisof specific algorithms,some universal interpretation strategies can be applied after modeltraining and treat a model as a black box without inspecting internalmodel parameters  2C.24,36,4.1145 SA can be performed with perturbation made to remove/permute oneor more features at a time. One simple approach is altering one-feature-at-a-time(OAT) to see changes of the outputs.146 However, the OAT approach does not fully explore the input spacesince it does not detect the interactions between input features.The variance-based method quantifies the contributions of input featuresto the variance of the model predictions by treating the input andoutput uncertainties as probability distributions.147 As a measure of sensitivity, the total effect index gives the totalvariance in output Y caused by a feature X and its interactions with any other input features, whichallows full explorations of the input space accounting for interactions.In a Bayesian network model to predict chemical modes of action (MoA)for aquatic toxicology, SA is applied to examine individual and multiplefeatures’ abilities to maximize MoA probabilities.148 Only highly influential features were usedto predict MoA, which reduced the model complexity and aided modelinterpretations. Feature effect plots is a powerful interpretationtool that visualizes the effects of features on the model’soutputs. The partial dependence plot (PDP) visualizes the averagepartial dependence between the predicted label and one or two featureswhile keeping all other features fixed.91 It can show whether the relationship between the prediction anda feature is linear, monotonic, or more complex. For example, in modelingchemicals’ P-glycoprotein (P-gp) transport,Svetnik et al. selected 49 descriptors with high feature importance,which were related to functional groups, to do PDP visualization.149 Trends in PDPs can indicate whether a functionalgroup tends to raise or lower P-gp activity and infer potential structure–activityrelationships. An individual conditional expectation (ICE) plot extendsPDP and visualizes the dependence of predictions on one or two featuresfor each instance separately, thereby resulting in one curve per instance.152 ICEs can reveal individual differences and identify subgroups andinteractions between model inputs. Goldstein et al. demonstrated theuse of ICE plots to analyze how different subjects respond to depressiontreatments in a clinical trial.150 A blackbox model was built to predict treatment response scores using 37personal features of the subjects and one binary treatment-type descriptor(cognitive therapy as 0 and paroxetine as 1). An ICE plot of two features,marital status and treatment type, showed that cognitive therapy isgenerally predicted to do better with married subjects, and paroxetineis predicted to do better with unmarried subjects. PDP and ICE plotsare easy to interpret; however, they may miss important features sincethe partial dependence of the examined features (up to two) is computedon the basis of the assumption that they are not correlated with otherfeatures.91 Several software tools havebeen developed to facilitate perturbation-based interpretations. Forexample, the iml R package implements many model-agnosticmethods, including PDP, ICE, and feature importance.153 The ICEbox and pdp Rpackages can be used for making ICE and PDP plots, respectively.154 In addition, several software tools have been developed to performsensitivity analysis, including the sensitivity Rpackage , the SAlib python library,155 and the SAFE  MATLAB package.156Perturbation-basedstrategy modifies or removes parts of feature data to measure thecorresponding change of the model output 2C. This 4.291 With an input data set and its correspondingoutput from a black box model, an interpretable surrogate model canbe trained. The performance of a surrogate model can be measured inapproximating predictions of the black box model and interpretingthe surrogate model. Examples of surrogate models include linear modelsfor characterizing linear relationships and decision trees and decisionrules for characterizing nonlinear relationships.158 A limitation of surrogate models is that complex black box modelscannot be well approximated. To address this, an approach known aslocal interpretable model-agnostic explanations (LIME) was developedto focus on a small subset of instances.159 LIME starts with instances of interest to generate a new data setconsisting of perturbed features and the corresponding output of ablack box model. Then, LIME trains an interpretable linear model,which is a good approximation of predictions for the instances ofinterest. The predictions of the black box model can be explainedby examining the parameters of the linear model. For example, in aDL modeling study for in vitro toxicity predictions,Ramsundar et al. applied LIME to extract potential toxicophores responsiblefor relevant toxicity.160 Sometimes, alinear surrogate model could lead to poor performance when the localrelationship is nonlinear. Another local approximation-based approach, anchors, has been developed to characterize nonlinear relationshipsusing decision rules.161 Anchor explanationsare effective in capturing nonlinear behaviors and can highlight thepart of feature data that is sufficient for making a prediction. The anchors method was implemented in the python package anchor,161 and integrated in alibi,162 a Python library forML model inspection and interpretation.Approximation-basedmethods involve learning an interpretable model  to approximate the output of a black box model 2C. Train590 For toxicity evaluations, an IML model can be developed to followthe toxicology knowledge  is the primary repository for international AOPdevelopment efforts coordinated by the Organisation for Economic Co-operationand Development (OECD)  and an adverse outcome in organisms 2B.59,166t (OECD) 2. Curren171 In a mechanistic model for hepatotoxicity predictions, the toxicitypotential of a chemical is assessed on the basis of whether it (1)possesses certain structural alerts and (2) activates the antioxidantresponse element (ARE) pathway, an oxidative stress-related key event.172 This is a decision rule-based model, wherechemicals that satisfy both two conditions are predicted as toxic,and chemicals that dissatisfy both two conditions are predicted asnontoxic. Chemicals that possess the identified structural alertsare suspected to be metabolized into reactive intermediates , which can trigger oxidative stress in the liver, thereby forminga plausible pathway that leads to hepatotoxicity. The limitation ofthis model is that it only can evaluate a small portion of hepatotoxicantssince other toxicity mechanisms besides oxidative stress can alsolead to hepatotoxicity. After the inclusion of additional assays representingkey events in other AOPs, some false negative predictions could becorrected.An individual AOP may focus on a specificpathway, where mechanistically linked events proceed to a toxicityeffect in a unidirectional and linear way.173 They can focus on a single adverse outcome but describe differentMIEs leading to this adverse outcome or share the same MIE but divergeto different AOPs.173 Judson et al. reporteda network model for chemical perturbations of the estrogen receptor(ER).174 This model integrated three associatedpathways, ER agonist, ER antagonist, and pseudoreceptor pathways,and utilized data from 18 HTS bioassays to identify ER agonists andantagonists. In a recent study, a knowledge base DNN model was developedto mimic the toxicity pathway for ER agonists using a virtual AOPframework.175 In the DNN architecture,57 HTS bioassays were organized among five network layers on the basisof the biological processes in the ER pathway—receptor bindingas MIE, receptor dimerization, DNA binding, transcriptional activation,and cell proliferation as key events—and eventually led tothe adverse outcome of in vivo rodent uterotrophicbioactivity. This model could efficiently and accurately evaluaterodent uterotrophic bioactivity for new compounds [area under thereceiver operating characteristic (ROC) curve (AUC) = 0.864–0.927],which outperformed the QSAR model that only used chemical structuredata as inputs (AUC = 0.594). Moreover, the model could virtuallysimulate the perturbations in the toxicity pathway for each predictedtoxic compound. The design of DNN models to mimic AOP networks ispromising for developing future interpretable models of complicatedtoxicity endpoints.Several AOPs sharing at least one common componentcan form anAOP network.5.2176 As part of systems biology, systems toxicology describes the toxicologicalevaluation of biological systems, which involves perturbating systemsby toxicants and stressors, monitoring molecular expressions and conventionaltoxicological parameters, and iteratively integrating response data.177 Toxicology programs, such as ToxCast/Tox21 and L1000, have madeprogress in integrating data from diverse technologies and endpointsin different levels into systems biology approaches for toxicity evaluations.81 Knowledge databases that reflect the functional characterizationof components and interactions among diverse components provide informatictools to support systems toxicology , whereas systemsbiology studies the molecular details of these biological systems using -omics technologies.xicology 2. For exxicology 2B.178,17rocesses2B.180 Th185 In VNNs, the connectivity of neurons in different layers is setto mirror the biological hierarchy. Genes or proteins as inputs onlyconnect to specific neurons representing their associated pathways,and these pathway neurons subsequently connect to their parent pathways,thereby making it a sparse DNN with reduced complexity and intrinsicinterpretability. Kuenzi et al. developed a VNN model named DrugCellto simulate the response of human cancer cells to therapeutic compounds.182 DrugCell was designed with two parts: a VNNmodeling the hierarchical organization of Gene Ontology terms anda conventional ANN embedding the chemical fingerprints. DrugCell couldcorrectly predict drug responses , which significantly outperformedthe elastic net regression model (p < 0.0001).Furthermore, DrugCell could provide insights into the underlying mechanismsof action by inspecting the simulated pathway neuron states. Elmarakebyet al. developed a VNN model named P-NET that integrates Reactomehierarchical knowledge to predict cancer state on the basis of patients’genomic profiles.184 The trained P-NETmodel outperformed classic ML models, including SVM, logistic regression,and decision trees, with AUC = 0.93 and accuracy = 0.83. Additionally,P-NET demonstrated significantly better performance than the traditionaldense DNN model in sample sizes up to 500 (p <0.05). In a recent study, Hao et al. reported a VNN model named DTox(deep learning for toxicology) for predict a chemical’s outcomesin 15 toxicity assays.183 DTox connectstarget protein profiles (input features) to toxicity assay outcomes(outputs) by hidden neurons mapping to the Reactome pathways. DToxcan achieve the same level of performance as classic ML approachesand further explain toxicity mechanisms by identifying VNN paths.Since the gene expression change of the components constituting apathway may reflect whether the pathway is perturbed, the identifiedVNN paths were further validated by differential expression analysisusing LINCS transcriptomic profiles.A recentdevelopment of a visible neural network (VNN) reported the integrationof ontological and pathway hierarchy in the design of interpretableDNN models.6In toxicology, ML-based computational modelingis a promising alternativemethod to traditional animal models for predicting chemical toxicitypotentials. In the current big data era, chemical toxicity data continueto grow at a rapid pace, and advanced ML and DL approaches are urgentlyneeded to deal with these data. Interpretability is critical for theapplication of ML in risk assessments of chemicals that may impacthuman and environmental health. In the above sections, we have presentedstrategies for applying IML in computational toxicology, includingthe use of interpretable feature data, interpretation methods, andthe development of intrinsic IML models using knowledgebase frameworksin toxicology.196 Many modeling studies use well-establishedmolecular representations as features, such as properties, binaryfingerprints, and geometrical descriptors, which can capture chemicaland structural features defined in advance.198 The generation of these molecular representations is standardized,and various structure curation protocols are available to facilitatethe chemical descriptor generations.200 However,as described above, toxicogenomic and assay data, which can captureintricate biological responses for chemicals of interest, are muchmore diverse, heterogeneous, and unstandardized than chemical structures.Experimental conditions and protocols for generating these data canvary widely among laboratories, thereby leading to poor data qualityin some data resources that may impact model performance and interpretability.7 Similarly, assay data from various studies andHTS programs may exhibit different data structures  and may haveinconsistent results for the same chemicals.201 When collecting training data from multiple resources, curationworkflows, such as those employed by Integrated Chemical Environmentand ChEMBL, should be implemented to ensure data quality and integrity.203Data standardization and curation are criticalin computationalmodeling approaches, where care should be taken to avoid introducingtechnical artifacts and to ensure the quality of modeling sets, aswell as the resulting model performance and interpretation.204 Traditionally, understandingwhich structure features of a toxicant contribute to its toxicityis useful to toxicologists for decision making and medicinal chemiststo modify the molecule.205 Mechanistic explanationsof the model predictions are crucial for high-stakes decision-making,such as determining whether a chemical is safe for humans and theenvironment. However, there are still many challenges to be resolved,and further efforts are needed to advance IML in this field. For example,new mechanistic IML models are trained on heterogeneous data , which increasesthe complexity of modeling tasks and makes it challenging to identifycritical features and explain underlying mechanisms. Another issuecaused by such heterogeneous data is the existence of missing valuesin the feature profiles for target compounds, which is a common issuein big data modeling.206 Methods to impute missing values may introduce uncertainties in the training dataand the following modeling procedure. Therefore, the development ofnovel and interpretable representations of chemicals will be criticalin future research.204 Intrinsic interpretablemodels that incorporate toxicological knowledge frameworks can overcomethe challenges posed by big data by providing both mechanism explanationsand accurate predictions.204 In QSAR modeling,the chemical descriptor space is normally used to define the applicabilitydomain of the model to assess whether the prediction for a targetchemical is reliable or not.209 However, it is difficult todefine the applicability domain for mechanistic models, especiallythose using DL and heterogeneous data. In terms of IML explanations,a challenge lies in how to measure the levels of interpretability,compare the interpretability of different IML models, or determinethe faithfulness of different interpretation methods to the same model.90 There are no universal criteria for selecting ML approaches fortoxicological modeling, nor is there a clear choice for the optimalinterpretation methods. Confidence in the interpretation results willbe enhanced when multiple approaches yield consistent conclusions.196 As many interpretation strategies being developedfor IML in the toxicology community, the use of these strategies canrequire significant computational knowledge for toxicologists. Furtherdevelopment and improvements of user-friendly software platforms canfacilitate the design, validation, and acceptance of IML and its associated explanations.The interpretation strategyshould be tailed to what needs to belearned from the model.