Background: Antipsychotic medications are crucial for managing psychiatric disorders such as schizophrenia, but their use can lead to side effects. This study compares the efficacy and side effects of monotherapy versus bi-therapy in the treatment of schizophrenia. Bi-therapy, also known as dual therapy or combination therapy, refers to the use of two medications simultaneously to treat a medical condition. Objective: This paper aims to evaluate the comparative efficacy and side effects of monotherapy (atypical antipsychotics) and bi-therapy (typical and atypical antipsychotics) over 12 months in schizophrenia patients. The objective is to compare monotherapy and bi-therapy in terms of symptom control (measured by PANSS), functional outcomes (measured by GAF), and side effects to determine which approach provides better overall treatment success in schizophrenia. Methods: A total of 100 schizophrenia patients were randomly assigned to two groups: Group A (monotherapy) and Group B (bi-therapy). The Positive and Negative Syndrome Scale (PANSS) and Global Assessment of Functioning (GAF) were used to assess symptom severity and functional outcomes at baseline and after 12 months. Side effects were also tracked. A machine learning model (Random Forest) was applied to identify key predictors of treatment success. Results: Group A (monotherapy) showed significant improvements in PANSS scores with fewer side effects. Group B (bi-therapy) showed greater symptom reduction but more pronounced side effects. Machine learning analysis identified PANSS scores at 12 months and side effects as the most important predictors of treatment success. Conclusion: Monotherapy with atypical antipsychotics offers a favorable balance of efficacy and side effects, making it a suitable option for many patients. Bi-therapy, while offering better symptom control, leads to more side effects and should be considered for treatment-resistant cases. Further studies are needed to optimize personalized treatment strategies using machine learning techniques.
References
[1]
Leucht, S., Corves, C., Arbter, D., Engel, R.R., Li, C. and Davis, J.M. (2009) Second-generation versus First-Generation Anti-psychotic Drugs for Schizophrenia: A Meta-analysis. The Lancet, 373, 31-41. https://doi.org/10.1016/s0140-6736(08)61764-x
[2]
Pierre, J.M. (2005) Extrapyramidal Symptoms with Atypical An-tipsychotics. Drug Safety, 28, 191-208. https://doi.org/10.2165/00002018-200528030-00002
[3]
Weiden, P.J. (2007) EPS Profiles: The Atypical Antipsychotics. Journal of Psychiatric Practice, 13, 13-24. https://doi.org/10.1097/00131746-200701000-00003
[4]
Parikh, S.V., Segal, Z.V., Grigoriadis, S., Ravindran, A.V., Kennedy, S.H., Lam, R.W., et al. (2009) Canadian Network for Mood and Anxiety Treatments (CANMAT) Clinical Guidelines for the Management of Major Depressive Disorder in Adults. II. Psychotherapy Alone or in Combination with Antidepres-sant Medication. Journal of Affective Disorders, 117, S15-S25. https://doi.org/10.1016/j.jad.2009.06.042
[5]
Denning, D.W., Pashley, C., Hartl, D., Wardlaw, A., Godet, C., Del Giacco, S., et al. (2014) Fungal Allergy in Asthma-State of the Art and Research Needs. Clinical and Translational Allergy, 4, Article 14. https://doi.org/10.1186/2045-7022-4-14
[6]
Mauri, M.C., Paletta, S., Maffini, M., Colasanti, A., Dragogna, F., Di Pace, C. and Altamura, A.C. (2014) Clinical Pharmacology of Atypical Antipsychotics: An Update. EXCLI Journal, 13, 1163-1191.
[7]
Chekroud, A.M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., et al. (2021) The Promise of Machine Learning in Predicting Treatment Outcomes in Psychiatry. World Psychiatry, 20, 154-170. https://doi.org/10.1002/wps.20882
[8]
Koutsouleris, N., Kahn, R.S., Chekroud, A.M., Leucht, S., Falkai, P., Wobrock, T., et al. (2016) Multisite Prediction of 4-Week and 52-Week Treatment Outcomes in Pa-tients with First-Episode Psychosis: A Machine Learning Approach. The Lancet Psychiatry, 3, 935-946. https://doi.org/10.1016/s2215-0366(16)30171-7
[9]
Wang, P., Li, Y. and Reddy, C.K. (2019) Machine Learning for Survival Analysis. ACM Computing Surveys, 51, 1-36. https://doi.org/10.1145/3214306
[10]
Ahmed, Z., Mohamed, K., Zeeshan, S. and Dong, X. (2020) Artificial Intelligence with Multi-Functional Machine Learning Platform Development for Better Healthcare and Precision Medicine. Database, 2020, baaa010. https://doi.org/10.1093/database/baaa010
[11]
Solmi, M., Murru, A., Pacchiarotti, I., Undurraga, J., Veronese, N., Fornaro, M., et al. (2017) Safety, Tolerability, and Risks Associated with First- and Second-Generation Antipsychotics: A State-Of-The-Art Clinical Review. Therapeutics and Clinical Risk Management, 13, 757-777. https://doi.org/10.2147/tcrm.s117321
[12]
Gareri, P., De Fazio, P., De Fazio, S., Marigliano, N., Ibbadu, G.F. and De Sarro, G. (2006) Adverse Effects of Atypical Antipsychotics in the Elderly: A Review. Drugs & Aging, 23, 937-956. https://doi.org/10.2165/00002512-200623120-00002
[13]
Awad, A.G. and Voruganti, L.N.P. (2004) Impact of Atypical Antipsychotics on Quality of Life in Patients with Schizophrenia. CNS Drugs, 18, 877-893. https://doi.org/10.2165/00023210-200418130-00004
[14]
Németh, G., Laszlovszky, I., Czobor, P., Szalai, E., Szatmári, B., Harsányi, J., et al. (2017) Cariprazine versus Risperidone Monotherapy for Treatment of Predominant Negative Symp-toms in Patients with Schizophrenia: A Randomised, Double-Blind, Controlled Trial. The Lancet, 389, 1103-1113. https://doi.org/10.1016/s0140-6736(17)30060-0
[15]
Vieta, E. and Goikolea, J.M. (2005) Atypical Antipsychotics: New-er Options for Mania and Maintenance Therapy. Bipolar Disorders, 7, 21-33. https://doi.org/10.1111/j.1399-5618.2005.00212.x
[16]
Biagi, E., Capuzzi, E., Colmegna, F., Mascarini, A., Brambilla, G., Ornaghi, A., et al. (2017) Long-Acting Injectable Antipsychotics in Schizophrenia: Literature Review and Practical Perspec-tive, with a Focus on Aripiprazole Once-Monthly. Advances in Therapy, 34, 1036-1048. https://doi.org/10.1007/s12325-017-0507-x
[17]
Wilkes, G.M. and Barton-Burke, M. (2019) 2020-2021 Oncology Nursing Drug Handbook. Jones & Bartlett Learning.
[18]
Chan, S.J., Stamp, L.K., Liebergreen, N., Ndukwe, H., Marra, C. and Treharne, G.J. (2019) Tapering Biologic Therapy for Rheumatoid Arthritis: A Qualitative Study of Patient Perspectives. The Patient—Patient-Centered Outcomes Research, 13, 225-234. https://doi.org/10.1007/s40271-019-00403-9
[19]
Waljee, A.K., Wallace, B.I., Cohen-Mekelburg, S., Liu, Y., Liu, B., Sauder, K., et al. (2019) Development and Validation of Machine Learning Models in Prediction of Remission in Patients with Moderate to Severe Crohn Disease. JAMA Network Open, 2, e193721. https://doi.org/10.1001/jamanetworkopen.2019.3721
[20]
Spechler, S.J., Lee, E., Ahnen, D., Goyal, R.K., Hira-no, I., Ramirez, F., et al. (2001) Long-term Outcome of Medical and Surgical Therapies for Gastroesophageal Reflux Disease: Follow-Up of a Randomized Controlled Trial. JAMA, 285, 2331-2338. https://doi.org/10.1001/jama.285.18.2331
[21]
Krishnan, P. (2024) AI-Driven Optimization in Healthcare: Machine Learning Models for Predictive Diagnostics and Personalized Treatment Strategies. Well Testing Journal, 33, 10-33.
[22]
Chabner, B.A. and Longo, D.L. (2011) Cancer Chemotherapy and Biotherapy: Principles and Practice. Lip-pincott Williams & Wilkins.
[23]
Plana, D., Palmer, A.C. and Sorger, P.K. (2022) Independent Drug Action in Combination Therapy: Implications for Precision Oncology. Cancer Discovery, 12, 606-624. https://doi.org/10.1158/2159-8290.cd-21-0212
[24]
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F. and Tighe, P. (2021) Accessing Artificial Intelligence for Clinical Decision-Making. Frontiers in Digital Health, 3, Article 645232. https://doi.org/10.3389/fdgth.2021.645232