A phase I study to assess the safety, pharmacokinetics (PK), pharmacodynamics (PD) and antitumor activities of BAL101553, a novel tumor checkpoint controller (TCC), administered as 48-hour infusion in adult patients with advanced solid tumors

TPS2602

Background: BAL101553 (prodrug of BAL27862), is a novel TCC that promotes tumor cell death by modulating the spindle assembly checkpoint. BAL27862 has shown potent antitumor activity in diverse preclinical tumor models, including models refractory to standard therapies. In a completed Phase 1 study using 2-h IV infusions (Days 1, 8, 15, q28d, NCT01397929 , CDI-CS-001, Lopez et al. JCO 34, 2016 suppl; 2525) dose-limiting vascular effects were observed and appeared Cmax related. The recommended Phase 2 dose for 2-h IV BAL101553 is 30 mg/m2. Vascular toxicity was not observed in an ongoing study with oral BAL101553 (NCT02490800, CDI-CS-002) at daily doses up to 30 mg (QD). Preclinical data suggest that antiproliferative effects of BAL101553/27862 are driven by exposure (AUC); thus vascular toxicity and antitumor activity are mediated by different PK drivers. BAL27862 has a half-life of ~15 h. Based on PK-modeling, extending the infusion from 2 h to 48 h was expected to result in ~4-fold higher AUC at a given Cmaxlevel and thereby improve the therapeutic window. Methods: This is an ongoing multicenter, open-label, Phase 1 dose-escalation study (NCT02895360, CDI-CS-003/SAKK67/15) using a 3+3 design to determine the MTD, characterize dose-limiting toxicities and assess the PK, PD and antitumor activities of 48-h infusions of BAL101553 in consecutive 28-day cycles at a starting dose of 30 mg/m2 administered on Day 1, 8 and 15 (q28d). The dose escalation scheme foresees up to ~ 50% dose increments depending on observed toxicities. During cycle 2, patients receive 7 days oral (QD) BAL101553 (Day 15–21) instead of the weekly IV infusion to assess absolute oral bioavailability. Patients with histologically-confirmed advanced or recurrent solid tumors are eligible for enrollment. Adverse events are assessed using CTCAEv4; tumor response by RECIST 1.1 (every 2 cycles). PD assessments include optional tumor biopsies and circulating tumor cells. PK profiles are assessed during the first 2 cycles. Two dose cohorts (30 and 45 mg/m2) have completed without DLTs or signs of vascular toxicity. Clinical trial information: NCT02895360.

ParamILS: An Automatic Algorithm Configuration Framework

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.

SATzilla: Portfolio-based Algorithm Selection for SAT

It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.