The immune response can be manipulated to suppress unwanted responses resulting from autoimmunity, allergy, and transplant rejection, and to stimulate protective responses against pathogens that largely elude the immune system (see immunization). Immunosuppressive drugs are used to control autoimmune disorders or inflammation when excessive tissue damage occurs, and to prevent transplant rejection after an organ transplant.[35][125]
Anti-inflammatory drugs are often used to control the effects of inflammation. The glucocorticoids are the most powerful of these drugs; however, these drugs can have many undesirable side effects (e.g., central obesity, hyperglycemia, osteoporosis) and their use must be tightly controlled.[126] Therefore, lower doses of anti-inflammatory drugs are often used in conjunction with cytotoxic or immunosuppressive drugs such as methotrexate or azathioprine. Cytotoxic drugs inhibit the immune response by killing dividing cells such as activated T cells. However, the killing is indiscriminate and other constantly dividing cells and their organs are affected, which causes toxic side effects.[125] Immunosuppressive drugs such as ciclosporin prevent T cells from responding to signals correctly by inhibiting signal transduction pathways.[127]
Larger drugs (>500 Da) can provoke a neutralizing immune response, particularly if the drugs are administered repeatedly, or in larger doses. This limits the effectiveness of drugs based on larger peptides and proteins (which are typically larger than 6000 Da). In some cases, the drug itself is not immunogenic, but may be co-administered with an immunogenic compound, as is sometimes the case for Taxol. Computational methods have been developed to predict the immunogenicity of peptides and proteins, which are particularly useful in designing therapeutic antibodies, assessing likely virulence of mutations in viral coat particles, and validation of proposed peptide-based drug treatments. Early techniques relied mainly on the observation that hydrophilic amino acids are overrepresented in epitope regions than hydrophobic amino acids;[128] however, more recent developments rely on machine learning techniques using databases of existing known epitopes, usually on well-studied virus proteins, as a training set.[129] A publicly accessible database has been established for the cataloguing of epitopes from pathogens known to be recognizable by B cells.[130] The emerging field of bioinformatics-based studies of immunogenicity is referred to as immunoinformatics.[131]. Immunoproteomics is a term used to describe the study of large sets of proteins (proteomics) involved in the immune response.
Anti-inflammatory drugs are often used to control the effects of inflammation. The glucocorticoids are the most powerful of these drugs; however, these drugs can have many undesirable side effects (e.g., central obesity, hyperglycemia, osteoporosis) and their use must be tightly controlled.[126] Therefore, lower doses of anti-inflammatory drugs are often used in conjunction with cytotoxic or immunosuppressive drugs such as methotrexate or azathioprine. Cytotoxic drugs inhibit the immune response by killing dividing cells such as activated T cells. However, the killing is indiscriminate and other constantly dividing cells and their organs are affected, which causes toxic side effects.[125] Immunosuppressive drugs such as ciclosporin prevent T cells from responding to signals correctly by inhibiting signal transduction pathways.[127]
Larger drugs (>500 Da) can provoke a neutralizing immune response, particularly if the drugs are administered repeatedly, or in larger doses. This limits the effectiveness of drugs based on larger peptides and proteins (which are typically larger than 6000 Da). In some cases, the drug itself is not immunogenic, but may be co-administered with an immunogenic compound, as is sometimes the case for Taxol. Computational methods have been developed to predict the immunogenicity of peptides and proteins, which are particularly useful in designing therapeutic antibodies, assessing likely virulence of mutations in viral coat particles, and validation of proposed peptide-based drug treatments. Early techniques relied mainly on the observation that hydrophilic amino acids are overrepresented in epitope regions than hydrophobic amino acids;[128] however, more recent developments rely on machine learning techniques using databases of existing known epitopes, usually on well-studied virus proteins, as a training set.[129] A publicly accessible database has been established for the cataloguing of epitopes from pathogens known to be recognizable by B cells.[130] The emerging field of bioinformatics-based studies of immunogenicity is referred to as immunoinformatics.[131]. Immunoproteomics is a term used to describe the study of large sets of proteins (proteomics) involved in the immune response.
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