Early Prediction Of Clinical Response Research Articles

Early Changes in Circulating Cell-Free KRAS G12C Predict Response to Adagrasib in KRAS Mutant Non-Small Cell Lung Cancer Patients.

Non-invasive monitoring of circulating tumor DNA (ctDNA) has the potential to be a readily available measure for early prediction of clinical response. Here, we report on early ctDNA changes of KRAS G12C in a Phase 2 trial of adagrasib in patients with advanced, KRAS G12C-mutant lung cancer. We performed serial droplet digital PCR (ddPCR) and plasma NGS on 60 KRAS G12C-mutant patients with lung cancer that participated in cohort A of the KRYSTAL-1 clinical trial. We analyzed the change in ctDNA at 2 specific intervals: Between cycles 1 and 2 and at cycle 4. Changes in ctDNA were compared with clinical and radiographic response. We found that, in general, a maximal response in KRAS G12C ctDNA levels could be observed during the initial approximately 3-week treatment period, well before the first scan at approximately 6 weeks. 35 patients (89.7%) exhibited a decrease in KRAS G12C cfDNA >90% and 33 patients (84.6%) achieved complete clearance by cycle 2. Patients with complete ctDNA clearance at cycle 2 showed an improved objective response rate (ORR) compared with patients with incomplete ctDNA clearance (60.6% vs. 33.3%). Furthermore, complete ctDNA clearance at cycle 4 was associated with an improved overall survival (14.7 vs. 5.4 months) and progression-free survival (HR, 0.3). These results support using early plasma response of KRAS G12C assessed at approximately 3 weeks to anticipate the likelihood of a favorable objective clinical response.

Early Detection of Therapeutic Benefit from PD-1/PD-L1 Blockade in Advanced Lung Cancer by Monitoring Cachexia-Related Circulating Cytokines

Cancer cachexia is associated with poor immunotherapeutic outcomes. This prospective observational study longitudinally evaluated the role of cachexia-related circulating cytokines in predicting the risk and benefit of PD-1/PD-L1 blockade in advanced lung cancer. Forty-one circulating cytokines at baseline and after one cycle of PD-1/PD-L1 blockade treatment were measured in patients with advanced lung cancer between 2019 and 2020. The cachexia-related cytokines were identified by comparing the levels of circulating cytokines between cachectic and non-cachectic patients. Among 55 patients, 49.1% were diagnosed with cachexia at the beginning of PD-1/PD-L1 blockade therapy. Baseline levels of the circulating cytokines IL-6, IL-8, IL-10, IL-15, and IP-10 were significantly higher in cachectic patients. In contrast, the level of eotaxin-1 was lower in cachectic patients than in those without cachexia. Higher IL-6 at baseline and during treatment was associated with a greater risk of immune-related adverse events, while higher IL-10 at baseline was linked to worse overall survival. More importantly, increased eotaxin-1 after one cycle of PD-1/PD-L1 blockade treatment was associated with higher objective response and better overall survival. A blood-based, cachexia-related cytokine assay may yield potential biomarkers for the early prediction of clinical response to PD-1/PD-L1 blockade and provide clues for improving the outcomes of cachectic patients.

Neoadjuvant chemotherapy modulates exhaustion of T cells in breast cancer patients.

Breast cancer is the leading cause of cancer deaths in women worldwide. It has been observed that the incidence of breast cancer increases linearly with age after 45, which suggest a link between cancer, aging, and senescence. A growing body of evidence indicates that the immunosuppressive tumor network in breast cancer patients can lead to T-cell exhaustion and senescence. Cytotoxic chemotherapy is a common treatment for many cancers, and it is hypothesized that its efficacy may be related to immune activation. However, the effects of neoadjuvant chemotherapy on T-cell dysfunction in breast cancer patients are not fully understood. This study aimed to evaluate the impact of neoadjuvant chemotherapy on the expression of exhaustion and senescence markers in T cells in women with breast cancer. Our results showed that T cells from breast cancer patients have a reduced ability to respond to stimulation in-vitro and an increased expression of senescence and exhaustion-associated markers, such as TIM-3, LAG3, and CD57. Furthermore, we found that neoadjuvant chemotherapy has an immunomodulatory effect and reduces the expression of exhaustion markers. Our observations of the immune phenotype of T cells during neoadjuvant chemotherapy treatment highlight its ability to stimulate the immune system against cancer. Therefore, monitoring the response of T cells during chemotherapy may enable early prediction of clinical response.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling.

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Early prediction of clinical response to anti-TNF treatment using multi-omics and machine learning in rheumatoid arthritis.

ObjectivesAdvances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition.MethodsPeripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment.ResultsThe gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA.ConclusionsOur integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.

Early Prediction of Clinical Response to Etanercept Treatment in Juvenile Idiopathic Arthritis Using Machine Learning.

Background and AimsAt present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis (JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record (EMR).Materials and MethodsEMR data of 87 JIA patients treated with etanercept between January 2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extremely Random Trees (ET) and Logistic Regression (LR) were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the model.ResultsTender joint count (TJC), Time interval, Lymphocyte percentage (LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%, specificity 66.67%, accuracy 72.22%, AUC 79.17%, respectively.ConclusionA pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.

In vitro drug sensitivity (IDS) of patient-derived primary osteosarcoma cells as an early predictor of the clinical outcomes of osteosarcoma patients.

Early prediction of clinical response to conventional chemotherapy is a significant factor in determining an overall treatment strategy for osteosarcoma. Cells were extracted from treatment-naïve biopsies from 16 osteosarcoma patients who received a doxorubicin and cisplatin-based neoadjuvant chemotherapy regimen and their sensitivities to doxorubicin and cisplatin were measured as IC50 values. Associations of in vitro drug sensitivity (IDS) levels and clinical outcomes were examined. Primary osteosarcoma cells responded to doxorubicin and cisplatin with IC50 values of 0.088 ± 0.032µM and 16.7 ± 8.5µM, respectively. The patients with a non-metastatic phenotype and surviving patients showed significantly lower IC50 values for both drugs. ROC analysis defined the optimal IC50 cut-off values for doxorubicin (IDSdox) and cisplatin (IDScpt) as 0.05µM (AUC 0.82) and 14µM (AUC 0.87), respectively. Survival analysis found significantly longer disease-free survival (DFS, n = 14) and overall survival (OS, n = 16) times in the patients with low IDSdox (p = 0.0064 for DFS and p = 0.0102 for OS) and low IDScpt (p = 0.0204 for DFS and p = 0.0021 for OS). Interestingly, when the patients with low IDScpt and those with low IDSdox were combined (Group 1), significant associations with prolonged DFS (p = 0.0042, C-statistic 0.78) and OS (p = 0.0010, C-statistic 0.79) were found. In this cohort, histological response to neoadjuvant chemotherapy could predict only OS. This study indicates that IDS analysis could potentially be a practical, rapid, and reliable technique for predicting clinical outcomes. It could also be used to identify patients for whom conventional chemotherapy is most appropriate and, in the future, help advance personalized therapy.

Early prediction of clinical response in first episode schizophrenia (FES) patients receiving olanzapine

Background: At present, schizophrenia guidelines recommend waiting for 8 weeks before considering a patient as non-responder. This study aims to detect the optimal early response threshold that best predict the final outcome of olanzapine.Methods: The study was conducted for 8-week, four points follow up (week 2,3,4, and 8) prospective observational study. A reduction of 20, 25, 30% in Positive and Negative Syndrome Scale (PANSS) score from the base line at week 2,3, and 4 respectively were considered as early response. A reduction of 50% at week 8 was considered as responders. Receiver Operating Characteristics (ROC) curves were performed to detect the optimal threshold.Results: Mean total baseline PANSS score was 106.66(95% CI; 100.4, 112.9). Week 2 (AUC = 50.5%, p > 0.964) and week 3 (AUC = 64.9, p > 0.13) responses failed to predict the 8th week response. Week 4 response (AUC = 92%, p < 0.001) can be taken for the prediction of 8th week response (specificity = 72%, sensitivity = 100%, Positive Predictive Value = 61.1%, Negative Predictive Value = 100% and Optimum Early Response (OER) = 29.4%). 25 patients (69%) achieved more than 50% reduction (responders) in PANSS score after 8 weeks of treatment.Conclusions: Our study suggests that patients with early response at week 4 are likely to achieve positive response after 8 weeks. This may help in appropriate clinical decision making for early non-responders.Key PointsThe early response can forecast the outcome at the endpoint for the treatment of FESA reduction of baseline PANSS score by 30% or more after four weeks are likely to have remission after week 8 with olanzapine therapy.

Molecular Measurable Residual Disease Testing of Blood During AML Cytotoxic Therapy for Early Prediction of Clinical Response.

Measurable residual disease (MRD) testing after initial chemotherapy treatment can predict relapse and survival in acute myeloid leukemia (AML). However, it has not been established if repeat molecular or genetic testing during chemotherapy can offer information regarding the chemotherapy sensitivity of the leukemic clone. Blood from 45 adult AML patients at day 1 and 4 of induction (n = 35) or salvage (n = 10) cytotoxic chemotherapy was collected for both quantitative real-time PCR (qPCR) assessment (WT1) and next generation sequencing (>500 × depth) of 49 gene regions recurrently mutated in MDS/AML. The median age of subjects was 62 (23–78); 42% achieved a complete response. WT1 was overexpressed in most patients tested but was uninformative for very early MRD assessment. A median of 4 non-synonymous variants (range 0–7) were detected by DNA sequencing of blood on day 1 of therapy [median variant allele frequency (VAF): 29%]. Only two patients had no variants detectable. All mutations remained detectable in blood on day 4 of intensive chemotherapy and remarkably the ratio of mutated to wild-type sequence was often maintained. This phenomenon was not limited to variants in DNMT3A, TET2, and ASXL1. The kinetics of NPM1 and TP53 variant burden early during chemotherapy appeared to be exceptions and exhibited consistent trends in this cohort. In summary, molecular testing of blood on day 4 of chemotherapy is not predictive of clinical response to cytotoxic induction therapy in AML. The observed stability in variant allele frequency suggests that cytotoxic therapy may have a limited therapeutic index for clones circulating in blood containing these mutations. Further validation is required to confirm the utility of monitoring NPM1 and TP53 kinetics in blood during cytotoxic therapy.

Molecular testing during AML treatment for early prediction of clinical response.

7047Background: Early prediction of response in acute myeloid leukemia (AML) patients undergoing cytotoxic chemotherapy may have clinical utility. Currently, measurable residual disease (MRD) testi...

Early Monitoring of Response (MORE) to Golimumab Therapy Based on Fecal Calprotectin and Trough Serum Levels in Patients With Ulcerative Colitis: A Multicenter Prospective Study.

BackgroundThe treatment of ulcerative colitis (UC) patients with moderate to severe inflammatory activity with anti-tumor necrosis factor alpha (TNFα) antibodies leads to a clinical remission rate of 10% after 8 weeks of therapy. However, it must be taken into account that patient selection in clinical trials clearly influences both response and remission rates. An unsatisfactory response to anti-TNFα medication after week 12 often leads to a discontinuation of treatment. The early prediction of clinical response could therefore help optimize therapy and potentially avoid ineffective treatments.ObjectiveThe aim of this study is to develop an algorithm for optimizing golimumab administration in patients with moderate to severe UC by calculating the probability of clinical response in Week 26 based on data from Week 6.MethodsThe study is designed as a prospective, single-arm, multicenter, non-interventional observational study with no interim analyses and a sample size of 58 evaluable patients. The primary outcome is the prediction of clinical response in Week 26 based on a 50% reduction in fecal calprotectin and a positive golimumab trough level in Week 6.ResultsEnrollment started in October 2014 and was still open at the date of submission. The study is expected to finish in December 2016.ConclusionsThe early identification of patients who are responding to an anti-TNFα antibody is therapeutically beneficial. At the same time, patients who are not responding can be identified earlier. The development of a therapeutic algorithm for identifying patients as responders or non-responders can thus help prescribing physicians to both avoid ineffective treatments and adjust dosages when necessary. This in turn promotes a higher degree of treatment tolerance and patient safety in the case of anti-TNFα antibody administration.ClinicalTrialGerman Clinical Trials Register, Deutsches Register Klinischer Studien DRKS00005940; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005940 (Archived by WebCite at http://www.webcitation.org/6i4Xoo1sH)

Early prediction of clinical response to first-line treatment with gefitinib using integrated FDG-PET for patients with advanced non-small cell lung cancer.

e18049 Background: Early prediction of clinical response is of great value in patients with advanced non-small cell lung cancer (NSCLC) in avoiding unnecessary toxicity and giving them another chance for different treatment. This study aimed to assess the integrated 18-fluorodeoxyglucose positron emission tomography (FDG-PET) at 3 days after treatment as a tool for early response predictor in the patients treated with gefitinib or carboplatin plus paclitaxel (CP). Methods: Patients with stage IIIB or IV NSCLC were scheduled to receive first-line treatment were eligible for this study. Patients were treated with gefitinib (250mg) once daily or with CP every 3-week cycle as first-line treatment. FDG-PET was performed before and 3 day after initiation of each treatment. Based on previous studies, a reduction of tumor FDG uptake was assessed by using standardized uptake value (SUV). ROC analyses were performed to compare the results of the visually interpreted FDG-PET findings and the findings obtained with the use of an SUV threshold. Results: A total 38 patients were included in this study. Nineteen patients (50%) received gefitinib, and 19 (50%) received CP. The overall response rate (RR) was 36% with 7 partial response (PR) in gefitinig group and 16% with 3 PR in CP group. The optimal differentiation achieved by a cut-off value in reduction of SUV level was 20% in gefitinib group and 15% in CPgroup. Applying this cut-off value as a criterion for a metabolic response, sensitivity and specificity for predicted clinical response was of 91% and 100% in patients with gefitinib, 81% and 67% in patients with CP, respectively. , Metabolic responder had significant longer survival than non-responder in gefitinib group (median overall survival (OS): 22.6m, 95%CI: 7.4-37.8 vs median OS: 7.7m, 95%CI: 1.7-13.7, p=0.04), while there was no correlation between a reduction of SUV level and OS in CP group (median OS: 13.9m, 95%CI: 10.0-17.8 vs median OS: 10.5m, 95%CI: 7.3-13.7, p=0.56). Conclusions: The reduction of SUV level by using FDG-PET at 3 day after treatment can predict response and survival in gefitinib-treated patients.

Early PET analysis to identify responders to erlotinib treatment of advanced NSCLC.

e18092 Background: EGFR mutations predict response to erlotinib treatment in advanced NSCLC. However, identification of EGFR mutations is not always possible due to lack of adequate tissue. We therefore investigated whether early PET might be a suitable tool to identify EGFR mutation positive responders. Methods: Six patients with NSCLC stage IIIB/IV showing partial remission according to RECIST criteria to first-line erlotinib treatment were analyzed and compared to 31 patients without response to first-line treatment with erlotinib. All patients were examined by [18F] FLT-PET and [18F] FDG-PET at baseline and after 1 week of treatment with erlotinib. The change in the sum of the maximum standard uptake value (sSUVmax) was calculated. Mutational status in each patient was analyzed by conventional Sanger sequencing. In case of negative results parallel sequencing using 454 technology was performed (EudraCT number 2005-005393-73; NCT00568841). Results: The median PFS for the 6 responding patients was 412 days. The mean reduction in the sSUV max was 37% (16-61) for [18F] FDG and 33% (0-55) for [18F] FLT after 1 week of treatment. Sanger sequencing of tissue available for molecular analyses revealed a sensitizing EGFR mutation in 3 of these 6 patients. Using the highly sensitve 454 technology an EGFR mutation could be demonstrated in 1 further patient. In the remaining two patients there was no diagnostic material allowing further molecular analyses. By comparison, the mean change in sSUVmax in patients not responding to erlotinib (n=31) was + 3% for [18F] FLT and -3% for [18F] FDG (p<0.005, T test). In the ROC analysis the AUC was calculated with 0.88 (0.73-1.00, p=0.004) for detection of response. Conclusions: Early [18F] FLT-PET and [18F] FDG-PET might be a suitable tool to detect patients with sensitizing EGFR mutations when treated with erlotinib. This might circumvent EGFR mutational analyses in patients with insufficient diagnostic material and enable early prediction of clinical response. Pat ID Histology Sanger 454 Change in sSUVmax LT-PET Change in sSUVmax FDPET 01-08 Adeno 0 del exon 19 -30 -44 01-18 Adeno del exon 19 N/A -44 -16 01-27 Adeno 0 N/A -55 -61 01-31 Adeno 0 N/A -39 -45 01-28 Adeno del exon 19 N/A 0 -38 01-25 Adeno L858R N/A -30 -22 Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Roche Roche Roche

Value of Hypoxic Volume Response by (18)F-FETNIM-PET in Esophageal Squamous Carcinoma for Early Prediction of Clinical Response with Definitive Chemoradiotherapy

Value of Hypoxic Volume Response by (18)F-FETNIM-PET in Esophageal Squamous Carcinoma for Early Prediction of Clinical Response with Definitive Chemoradiotherapy

P.2.c.019 Early prediction of clinical response in patients with major depressive disorder receiving fluoxetine

Nefazodone has been widely used as an antidepressant, but it has not been tested for depression with anger attacks. In an open study, we administered nefazodone (maximum 600 mg/day) for 12 weeks to 16 outpatients who had major depression with anger attacks. Assessment instruments comprised the Structured Clinical Interview for DSM-IV (SCID), Anger Attacks Questionnaire (AAQ), 17-item Hamilton Rating Scale for Depression (HAM-D-17), Clinician Global Impression Scale (CGI), Symptom Questionnaire (SQ), Modified Overt Aggression Scale (MOAS), and MOAS-Self-Rated. Three subjects underwent positron emission tomography (PET) with [18F]-setoperone for 5-HT2 binding potential (BP) and [11C]-SCH-23,390 for D1 BP, both at baseline and after 6 weeks of treatment. Eight subjects underwent PET with [18F]-setoperone and with [11C]-SCH-23,390 at baseline only. In an examination of whether D1 and 5HT2 (data available in six subjects) receptor BP predicted treatment response, we found significant decreases in the HAM-D-17, CGI-S, weighted MOAS, MOAS verbal scale, OAS Self-Rated verbal, SQ Depression and Anger/Hostility scales after nefazodone; 50% responded to nefazodone (defined as ≥50% decrease in HAM-D-17 score), and 44% reported disappearance of anger attacks. A statistically significant percentage decrease in 5HT2 BP was observed for the right mesial frontal and left parietal regions after 6 weeks of treatment. No significant change was observed in D1 BP in any region. Although CGI-I scores correlated significantly with D1 BP in the left thalamic region, the correlation was not significant after Bonferroni correction. The effectiveness of nefazodone for depression with anger attacks may be related to widespread changes in 5HT2 receptor BP.

Early Prediction of Clinical Response in Schizophrenia Patients Receiving the Atypical Antipsychotic Zotepine

Prior early prediction models for antipsychotic treatment response demonstrate good specificity but poor sensitivity (i.e., high false-negative rates). The purpose of this study was to refine the early prediction model in schizophrenia patients taking an atypical antipsychotic agent, zotepine. 135 acutely ill inpatients with DSM-IV-defined schizophrenia received 4 weeks of 150 mg/day zotepine treatment. Psychopathology severity was assessed weekly with the Brief Psychiatric Rating Scale (BPRS) and subscales for positive, negative, and general symptoms. Clinical response was defined as a reduction of 20% or more in the BPRS total score at week 4. A logistic regression model was used to obtain early predictors. The receiver operating characteristic curve was employed to determine the optimal cutoff points of the variables for predicting response. The study was conducted from June 2004 to April 2005. The most significant early predictors for ultimate response at week 4 were BPRS positive subscale score changes at week 1 and, better, at week 2 (p < .001 at both timepoints). At week 1, a BPRS positive score reduction of 4 appeared to be the optimal cutoff point for predicting eventual response, providing a sensitivity of 0.77 and specificity of 0.77. At week 2, a BPRS positive score reduction of 6 was the best for prediction, with a sensitivity of 0.83 and specificity of 0.91. These findings suggest that using the first 2 weeks' improvement in positive symptoms to predict the fourth week's treatment response is favorable in terms of both specificity and sensitivity. Further studies are needed. Moreover, whether this model could be applied to establish a prediction system for other antipsychotics or other psychotropics also deserves research.