Safety and pharmacokinetics of MM-302, a HER2-targeted antibody-liposomal doxorubicin conjugate, in patients with advanced HER2-positive breast cancer: a phase 1 dose-escalation study.

BACKGROUND: This phase 1 dose-escalation trial studied MM-302, a novel HER2-targeted PEGylated antibody-liposomal doxorubicin conjugate, in HER2-positive locally advanced/metastatic breast cancer. METHODS: Patients were enrolled in four cohorts: MM-302 monotherapy (8, 16, 30, 40, and 50 mg/m2 every 4 weeks [q4w]); MM-302 (30 or 40 mg/m2 q4w) plus trastuzumab (4 mg/kg q2w); MM-302 (30 mg/m2) plus trastuzumab (6 mg/kg) q3w; MM-302 (30 mg/m2) plus trastuzumab (6 mg/kg) and cyclophosphamide (450 mg/m2) q3w. RESULTS: Sixty-nine patients were treated. The most common adverse events (AEs) were fatigue and nausea. Grade 3/4 AEs of special interest included neutropenia, fatigue, mucosal inflammation, anemia, thrombocytopenia, febrile neutropenia, and palmar-plantar erythrodysesthesia. The MTD was not reached. With MM-302 ≥ 30 mg/m2, overall response rate (ORR) was 13% and median progression-free survival (mPFS) 7.4 months (95% CI: 3·5-10·9) in all arms. In 25 anthracycline-naïve patients, ORR was 28·0% and mPFS 10·9 months (95% CI: 1·8-15·3). Imaging with 64Cu-labeled MM-302 visualized tumor-drug penetrance in tumors throughout the body, including the brain. CONCLUSION: MM-302 monotherapy, in combination with trastuzumab, or trastuzumab plus cyclophosphamide, was well tolerated and showed promising efficacy. The selected phase 2 MM-302 dose was 30 mg/m2 plus 6 mg/kg trastuzumab q3w.

Seribantumab, an Anti-ERBB3 Antibody, Delays the Onset of Resistance and Restores Sensitivity to Letrozole in an Estrogen Receptor-Positive Breast Cancer Model.

Heregulin-driven ERBB3 signaling has been implicated as a mechanism of resistance to cytotoxic and antiendocrine therapies in preclinical breast cancer models. In this study, we evaluated the effects of seribantumab (MM-121), a heregulin-blocking anti-ERBB3 monoclonal antibody, alone and in combination with the aromatase inhibitor letrozole, on cell signaling and tumor growth in a preclinical model of postmenopausal estrogen receptor-positive (ER(+)) breast cancer. In vitro, heregulin treatment induced estrogen receptor phosphorylation in MCF-7Ca cells, and long-term letrozole-treated (LTLT-Ca) cells had increased expression and activation levels of EGFR, HER2, and ERBB3. Treatment with seribantumab, but not letrozole, inhibited basal and heregulin-mediated ERBB receptor phosphorylation and downstream effector activation in letrozole-sensitive (MCF-7Ca) and -refractory (LTLT-Ca) cells. Notably, in MCF-7Ca-derived xenograft tumors, cotreatment with seribantumab and letrozole had increased antitumor activity compared with letrozole alone, which was accompanied by downregulated PI3K/MTOR signaling both prior to and after the development of resistance to letrozole. Moreover, the addition of an MTOR inhibitor to this treatment regimen did not improve antitumor activity and was not well tolerated. Our results demonstrate that heregulin-driven ERBB3 signaling mediates resistance to letrozole in a preclinical model of ER(+) breast cancer, suggesting that heregulin-expressing ER(+) breast cancer patients may benefit from the addition of seribantumab to antiendocrine therapy.

A viral dynamic model for treatment regimens with direct-acting antivirals for chronic hepatitis C infection.

We propose an integrative, mechanistic model that integrates in vitro virology data, pharmacokinetics, and viral response to a combination regimen of a direct-acting antiviral (telaprevir, an HCV NS3-4A protease inhibitor) and peginterferon alfa-2a/ribavirin (PR) in patients with genotype 1 chronic hepatitis C (CHC). This model, which was parameterized with on-treatment data from early phase clinical studies in treatment-naïve patients, prospectively predicted sustained virologic response (SVR) rates that were comparable to observed rates in subsequent clinical trials of regimens with different treatment durations in treatment-naïve and treatment-experienced populations. The model explains the clinically-observed responses, taking into account the IC50, fitness, and prevalence prior to treatment of viral resistant variants and patient diversity in treatment responses, which result in different eradication times of each variant. The proposed model provides a framework to optimize treatment strategies and to integrate multifaceted mechanistic information and give insight into novel CHC treatments that include direct-acting antiviral agents.

A multi-variant, viral dynamic model of genotype 1 HCV to assess the in vivo evolution of protease-inhibitor resistant variants.

Variants resistant to compounds specifically targeting HCV are observed in clinical trials. A multi-variant viral dynamic model was developed to quantify the evolution and in vivo fitness of variants in subjects dosed with monotherapy of an HCV protease inhibitor, telaprevir. Variant fitness was estimated using a model in which variants were selected by competition for shared limited replication space. Fitness was represented in the absence of telaprevir by different variant production rate constants and in the presence of telaprevir by additional antiviral blockage by telaprevir. Model parameters, including rate constants for viral production, clearance, and effective telaprevir concentration, were estimated from 1) plasma HCV RNA levels of subjects before, during, and after dosing, 2) post-dosing prevalence of plasma variants from subjects, and 3) sensitivity of variants to telaprevir in the HCV replicon. The model provided a good fit to plasma HCV RNA levels observed both during and after telaprevir dosing, as well as to variant prevalence observed after telaprevir dosing. After an initial sharp decline in HCV RNA levels during dosing with telaprevir, HCV RNA levels increased in some subjects. The model predicted this increase to be caused by pre-existing variants with sufficient fitness to expand once available replication space increased due to rapid clearance of wild-type (WT) virus. The average replicative fitness estimates in the absence of telaprevir ranged from 1% to 68% of WT fitness. Compared to the relative fitness method, the in vivo estimates from the viral dynamic model corresponded more closely to in vitro replicon data, as well as to qualitative behaviors observed in both on-dosing and long-term post-dosing clinical data. The modeling fitness estimates were robust in sensitivity analyses in which the restoration dynamics of replication space and assumptions of HCV mutation rates were varied.

Rapid decrease of wild-type hepatitis C virus on telaprevir treatment.

BACKGROUND: Telaprevir (TVR) is a hepatitis C virus (HCV) NS3.4A protease inhibitor that has exhibited antiviral activity in patients with HCV genotype 1 infection. The viral dynamics in patients dosed with TVR were compared with those reported for patients treated with interferon (IFN). METHODS: The dynamics of wild-type HCV genotype 1 in patients dosed with TVR monotherapy (n=36) and TVR plus pegylated interferon (PEG-IFN)-alpha2a (n=8) were quantified using a biphasic viral dynamic model. RESULTS: Patients dosed with either TVR monotherapy or TVR plus PEG-IFN-alpha2a had median first and second phase decreases of 12 per day and 1.1 per day, respectively. The second phase decrease was approximately 10-fold higher than reported values for IFN-based treatments (P<0.0001). Patients dosed with TVR plus PEG-IFN-alpha2a had a median remaining viral production after blockage (1-epsilon) of -2.37 log(10). In patients dosed with TVR monotherapy, increased TVR dosage of the same schedule was related to better blockage. CONCLUSIONS: These results suggested that TVR-based regimens for chronic HCV infection will lead to an early and more rapid viral decrease that could potentially result in higher sustained viral response rates as well as offer the potential for a reduced duration of treatment.

Natural prevalence of hepatitis C virus variants with decreased sensitivity to NS3.4A protease inhibitors in treatment-naive subjects.

BACKGROUND: The prevalence and clinical implications of naturally occurring variants that are resistant to hepatitis C virus (HCV) protease inhibitors in treatment-naive patients has not been reported. We report here the prevalence of such variants and their effect on clinical response. METHODS: Population sequence analysis of the NS3.4A protease was conducted in 570 treatment-naive subjects. RESULTS: Most subjects (98%) had wild-type virus. The remaining subjects had the following variants present in significant proportions (100%): V36M, 0.9%; R155K, 0.7%; V170A, 0.2%; and R109K, 0.2%. The V36M, R109K, and V170A substitutions confer low-level resistance (<7-fold) to protease inhibitors in replicon cells. The R155K substitution confers low-level resistance to telaprevir (TVR) and boceprevir and confers high-level resistance (>70-fold) to BILN 2061 and ITMN-191. Five subjects with the V36M or R109K variant were treated with 8-24 weeks of TVR and peginterferon-alpha2a (P) with or without ribavirin (R). Four achieved a sustained viral response, and 1 was lost to follow-up. In subjects with the R155K variant, TVR/PR provided greater antiviral activity than PR alone; however, the antiviral response was lower than that observed in subjects with wild-type virus. CONCLUSION: High levels of naturally occurring protease inhibitor-resistant variants were uncommon (<1% each) in HCV treatment-naive patients. TVR/PR efficiently inhibited V36M and R109K variants and contributed partial antiviral activity against the R155K variant. As new HCV agents are evaluated in clinical trials, it will be important to monitor the effect of baseline variants on sensitivity.

Biological network design strategies: discovery through dynamic optimization.

An important challenge in systems biology is the inherent complexity of biological network models, which complicates the task of relating network structure to function and of understanding the conceptual design principles by which a given network operates. Here we investigate an approach to analyze the relationship between a network structure and its function using the framework of optimization. A common feature found in a variety of biochemical networks involves the opposition of a pair of enzymatic chemical modification reactions such as phosphorylation-dephosphorylation or methylation-demethylation. The modification pair frequently adjusts biochemical properties of its target, such as activating and deactivating function. We applied optimization methodology to study a reversible modification network unit commonly found in signal transduction systems, and we explored the use of this methodology to discover design principles. The results demonstrate that different sets of rate constants used to parameterize the same network topology represent different compromises made in the resulting network operating characteristics. Moreover, the same topology can be used to encode different strategies for achieving performance goals. The ability to adopt multiple strategies may lead to significantly improved performance across a range of conditions through rate modulation or evolutionary processes. The optimization framework explored here is a practical approach to support the discovery of design principles in biological networks.