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Objectives: Peptide immunotherapy has been successfully applied as a therapeutic procedure for several immune-mediated diseases. Empirical observations showed that standard statistical approach is not an appropriate tool for the determination of prognostic parameters during peptide therapy. Therefore, we applied machine learning approach based on the C4.5 decision tree as a classifier. The method has been tested on the model of peptid-M (PENK_HUMAN 100-104 aa) vaccination in multiple sclerosis and optic neuritis. Methods: C4.5 decision tree has been tested on the model of peptid-M (Lupex) therapy in multiple sclerosis/optic neuritis. Results: Decision rules generated by the classifier extracted relationships between different parameters relevant for the prediction of beneficial peptide effects. The training set for the decision tree generator consisted of 38 tests observed before and one month after the peptide administration. Predictive parameters were EDSS, IFN, sIL-2R, sCD23 and peripheral blood cell populations CD20+23+, CD8+, CD8+beta2-M+, CD4+, CD4+b2-M+, CD4+25+ and CD3+16+56+. Conclusion: The accuracy of the procedure with respect to the therapy was 92- 100% for small samples. This model of non-linear prediction provided useful alternative to the standard statistical approach, enabled the extraction of few relevant parameters or their mutual relationships and ensured accurate prediction of the therapeutic procedure. The data were comparable to the clinical amelioration evaluated by the improvement of EDSS, VEP, colour vision, visual fields and MRI findings, 6 months and one year following the beginning of treatment.

Peptid-M is a small molecular mass neuropeptie (PENK, 100-104 aa), present in low quantity in different tissue including thymus. We analysed the results of peptid-M therapy in 21 patients with anterior uveitis, Behçet's disease, intermediate uveitis, optic neuritis and elapsing-remitting multiple sclerosis. Peptid-M (Lupex, Biofactor, Germany) was administered s.c. 0.25 mg/kg 5 times weekly for 4 weeks, then gradually reduced to 0.25 mg/kg weekly devided in 2 doses for >6 months. After peptide-M administration we observed marked rise in serum IFN levels, modulation of phagocite functions, NK activity, serum IL-4 and lowering of CD3+CD16+56 + cells. Clinical amelioration was noticed in all patients with uveitis and optic neuritis and in 10/12 MS patients. Improvements of VEP, colour vision, visual field abnormalities, EDSS and MRI findings were observed in most of the patients.

Peptide therapy has been successfully applied as useful procedure for the treatment of several immune-mediated diseases. Standatd statistical approach based on the comparison of individual parameters is not always appropriate tool for the determination of prognostic parameters defining the response to peptide application. We used another data analysis based on the non-linear machine learning approach with C4.5 decision tree as a classifier. The method has been tested on the model of met-enkephalin, i.e., peptid-M, (LUPEX) vaccination in multiple sclerosis and optic neuritis.

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Disturbance of cell-cytokine network are though to be responsible for the development of the immune-mediated nervous system diseases. Selective modulation of the immune response is a method recently applied for the treatment of those diseases. The aim of this modelling procedure is to: restore the physiological equilibrium between different cell populations, enable proper communication between cells via cytokines or other cell messages and ensure recognition/distinction between self and non-self tissue structures.

The application of peptid-M (Lupex) for immunotherapy in multiple sclerosis, optic neuritis and uveitis is described.