Experiments to determine the hydrolysis and protein binding of melphalan (L-phenylalanine mustard, L-PAM) were carried out in vitro for therapeutic concentration of the drug: the decrease in L-PAM concentration in plasma and whole blood during 24 h incubation at 37 degrees C was only 5% due to hydrolysis. Serum protein binding was about 90%, whereby 60% and 20% of this binding was due to interactions with albumin and acid alpha 1-glycoprotein, respectively. Immunoglobulins did not participate in the binding of L-PAM. The covalently bound part of L-PAM in serum was 30% in the concentration range of 1-30 micrograms/ml. The binding of dihydroxymelphalan (DOH) in serum did not exceed 20%. Glucocorticoids used in combination with L-PAM for treating multiple myeloma did not influence its protein binding. Our study with 35 sera from 15 patients with multiple myeloma shows that high levels of paraproteins do not increase but may decrease the binding of L-PAM, resulting in an elevated concentration of free drug.

Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inhibitor (JQ1) were conducted by quantitatively measuring drug effect using a composite AML disease inhibition score. 93% of predicted disease inhibition scores matched the associated ex vivo IC 50 value. Sensitivity and specificity of CBM predictions were 97.67%, and 64.29%, respectively. Genomic predictors of response were identified. Patient samples harbouring chromosomal aberrations del(7q) or −7, +8, or del(5q) and somatic mutations causing ERK pathway dysregulation, responded to JQ1 in both in silico and ex vivo assays. This study shows how a combination of genomics, computational modeling and chemosensitivity testing can identify network signatures associating with treatment response and can inform priority populations for future clinical trials of BET inhibitors.

Background: Multiple myeloma (MM) is an incurable heterogeneous hematological malignancy in which immune suppression and complex biology affect the disease and its response to treatment. Bortezomib (btz) and lenalidomide (len) alone or in combination with dexamethasone (dex) or other agents, are the predominant treatments for newly diagnosed and relapsed MM. Unfortunately, no precise method exists to predict disease response, making MM patient management difficult. Predicting treatment response would improve treatment effectiveness, and potentially reduce unnecessary treatment-related adverse events and health care costs. Aim: To determine the application of a genomics-informed predictive simulation model in MM patients treated with btz or len in combination with dex. Methods: Fourteen patients were selected from two datasets. Nine relapsed MM patients were identified from Washington University and 5 newly diagnosed MM patients were identified from the publicly accessible MMRF CoMMpass dataset. In all cases, whole exome sequencing and array CGH were performed. For each patient, every available genomic abnormality was entered into a computational biology program (Cellworks Group) that uses PubMed and other online resources to generate patient-specific protein network maps of activated and inactivated protein pathways (Doudican, et al, J Transl Med, 2015). Digital drug simulations with HMAs were conducted by quantitatively measuring drug effect on a composite MDS disease inhibition score (i.e., cell proliferation, viability, and apoptosis). Clinically, patients received standard of care treatment and clinical responses were recorded. Predictive values were calculated based on comparisons of the computer predictions and actual clinical outcomes. Results: The models predicted that 9 patients would respond to combination treatment and 5 would not. All response predictions were properly matched to their clinical response, resulting in 100% PPV, NPV, sensitivity, specificity, and accuracy. Interestingly, the model predicted that 6 of the 9 responders would not have responded to btz or len alone; instead, response was predicted to combination therapy with dex. Conclusions: Computational biology for MM demonstrated high predictive value for response to btz and len with dex. The model may be useful in uncovering the mechanisms for treatment failure and highlight additional pathways that could be targeted to increase chemosensitivity. Disclosures Vij: Jazz: Consultancy; Shire: Consultancy; Amgen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy; Bristol-Myers Squibb: Consultancy; Janssen: Consultancy; Novartis: Consultancy; Karyopharma: Consultancy. Vali:Cellworks Group: Employment. Abbasi:Cellworks: Employment. Kumar Singh:Cellworks: Employment. Kumar:Cellworks group: Employment. Gera:Cellworks: Employment.