title: Risk assessment in immunotoxicology. II. Relationships between immune and host resistance tests.
abstract: We have reported on the design and content of a screening battery using a "tier" approach for detecting potential immunotoxic compounds in mice (Luster et al., Fundam. Appl. Toxicol., 10, 2-19, 1988). The data base generated from these studies, which consists of over 50 selected compounds, has been collected and analyzed in an attempt to improve future testing strategies and provide information to aid in developing future quantitative risk assessment for immunotoxicity. In a recent study it was shown that as few as two or three immune parameters were needed to predict immunotoxicants in mice (Luster et al., Fundam. Appl. Toxicol., 18, 200-210, 1992). In particular, enumeration of lymphocyte populations and quantitation of the T-dependent antibody response were particularly beneficial. Furthermore, commonly employed apical measures (e.g., leukocyte counts, lymphoid organ weights) were fairly insensitive. The present analyses focus on the use of this data base to develop statistical models that examine the qualitative and quantitative relationship(s) between the immune function and host resistance tests. The conclusion derived from these analyses are: (1) A good correlation exists between changes in the immune tests and altered host resistance in that there were no instances where host resistance was altered without affecting an immune test(s). However, in some instances immune changes occurred without corresponding changes in host resistance. (2) No single immune test could be identified which was fully predictive for altered host resistance, although most assays were relatively good indicators (i.e., > 70%). Several others, such as proliferative response to lipopolysaccharide and leukocyte counts, were found to be relatively poor indicators for host resistance changes. (3) The ability to resist infectious agent challenge is dependent upon the degrees of immunosuppression and the quantity of infectious agent administered. (4) Logistic and standard regression modeling using one extensive chemical data set from the immunosuppressive agent, cyclophosphamide, indicated that most immune function-host resistance relationships followed linear rather than linear-quadratic (threshold-like) models. For most of the relationships this could not be confirmed using a large chemical data set and, thus, a more mechanistically based approach for modeling will need to be developed. (5) Using this limited data set, methods were developed for modeling the precise quantitative relationships between changes in selected immune tests and host resistance tests.
authors.affiliation: Environmental Immunology and Neurobiology Section, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709.
authors.forename: M I
authors.initials: MI
authors.lastname: Luster
authors.forename: C
authors.initials: C
authors.lastname: Portier
authors.forename: D G
authors.initials: DG
authors.lastname: Pait
authors.forename: G J
authors.initials: GJ
authors.lastname: Rosenthal
authors.forename: D R
authors.initials: DR
authors.lastname: Germolec
authors.forename: E
authors.initials: E
authors.lastname: Corsini
authors.forename: B L
authors.initials: BL
authors.lastname: Blaylock
authors.forename: P
authors.initials: P
authors.lastname: Pollock
authors.forename: Y
authors.initials: Y
authors.lastname: Kouchi
authors.forename: W
authors.initials: W
authors.lastname: Craig
mesh_terms: D000818:Animals; D003520:Cyclophosphamide / Q000633:toxicity; D004305:Dose-Response Relationship, Drug; D005260:Female; D007107:Immune System / Q000187:drug effects*; D007113:Immunity, Innate; D051379:Mice; D008810:Mice, Inbred C57BL; D015233:Models, Statistical; D012044:Regression Analysis; D012307:Risk Factors; D014116:Toxicology / Q000379:methods*
chemical_list: D003520:Cyclophosphamide