Ixazomib plus daratumumab and dexamethasone: Final analysis of a phase 2 study among patients with relapsed/refractory multiple myeloma.

Novel therapies have improved outcomes for multiple myeloma (MM) patients, but most ultimately relapse, making treatment decisions for relapsed/refractory MM (RRMM) patients increasingly challenging. We report the final analysis of a single-arm, phase 2 study evaluating the oral proteasome inhibitor (PI) ixazomib combined with daratumumab and dexamethasone (IDd; NCT03439293). Sixty-one RRMM patients (ixazomib/daratumumab-naïve; 1-3 prior therapies) were enrolled to receive IDd (28-day cycles) until disease progression/unacceptable toxicity. Median age was 69 years; 14.8% of patients had International Staging System stage III disease; 14.8% had received three prior therapies. Patients received a median of 16 cycles of IDd. In 59 response-evaluable patients, the overall response rate was 64.4%; the confirmed ≥very good partial response (VGPR) rate (primary endpoint) was 30.5%. Rates of ≥VGPR in patient subgroups were: high-risk cytogenetics (n = 15, 26.7%), expanded high-risk cytogenetics (n = 24, 29.2%), aged ≥75 years (n = 12, 16.7%), lenalidomide-refractory (n = 21, 28.6%), and prior PI/IMiD therapy (n = 58, 31.0%). With a median follow-up of 31.6 months, median progression-free survival was 16.8 months (95% confidence interval: 10.1-23.7). Grade ≥3 treatment-emergent adverse events (TEAEs) occurred in 54.1% of patients; 44.3% had serious TEAEs; TEAEs led to dose modifications/reductions/discontinuations in 62.3%/36.1%/16.4%. There were five on-study deaths. Any-grade and grade ≥3 peripheral neuropathy occurred in 18.0% and 1.6% of patients. Quality of life was generally maintained throughout treatment. IDd showed a positive risk-benefit profile in RRMM patients and was active in clinically relevant subgroups with no new safety signals.

Dose Titration of Ixazomib Maintenance Therapy in Transplant-Ineligible Multiple Myeloma: Exposure-Response Analysis of the TOURMALINE-MM4 Study.

Ixazomib has been approved in several countries as single-agent maintenance therapy in newly diagnosed multiple myeloma, in both posttransplant and transplant-ineligible settings, based on two phase III studies. In these maintenance studies, patients were initially administered 3 mg ixazomib, escalating to 4 mg if the initial dose level was well tolerated through Cycles 1-4. Here, we report the results of exposure-response analyses of TOURMALINE-MM4, wherein relationships between exposure and clinical response, dose adjustments, and selected adverse events were evaluated. Similar progression-free survival benefits were observed across the range of ixazomib exposures achieved in the study. Moreover, increased ixazomib exposures corresponded to a higher probability of maintaining complete response. Exposure was not a significant predictor (P > 0.05) of hematological adverse events (anemia, neutropenia, thrombocytopenia) and peripheral neuropathy; however, higher exposures did correlate to increased probabilities of experiencing diarrhea, vomiting, nausea, rash, and fatigue. While ixazomib exposure was not predictive of dose reductions, lower apparent clearance values (corresponding to higher systemic exposures) were correlated with a reduced likelihood of escalating to the 4 mg dose. Thus, the dose titration approach balanced patient benefit and risk; it ensured that only patients for whom the 3 mg dose was safe/tolerable escalated to the higher dose, while maximizing the fraction of patients (85%) who were able to derive additional clinical benefit at 4 mg. Collectively, these results highlight the value of safety-driven personalized dosing to maximize patient benefit/risk.

Population pharmacokinetic/pharmacodynamic joint modeling of ixazomib efficacy and safety using data from the pivotal phase III TOURMALINE-MM1 study in multiple myeloma patients.

Ixazomib is an oral proteasome inhibitor approved in combination with lenalidomide and dexamethasone for the treatment of relapsed/refractory multiple myeloma (MM). Approval in the United States, Europe, and additional countries was based on results from the phase III TOURMALINE-MM1 (C16010) study. Here, joint population pharmacokinetic/pharmacodynamic time-to-event (TTE) and discrete time Markov models were developed to describe key safety (rash and diarrhea events, and platelet counts) and efficacy (myeloma protein [M-protein] and progression-free survival [PFS]) outcomes observed in TOURMALINE-MM1. Models reliably described observed safety and efficacy results; prior immunomodulatory drug therapy and race were significant covariates for diarrhea and rash events, respectively, whereas M-protein dynamics were sufficiently characterized using TTE models of relapse and dropout. Moreover, baseline M-protein was identified as a significant covariate for observed PFS. The developed framework represents an integrated approach to describing safety and efficacy with MM therapy, enabling the simulation of prospective trials and potential alternate dosing regimens.

Author Correction: Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control.

This Article contains errors in Supplementary Table 3, which are described in the Author Correction associated with this Article. The simulation results in the Article were based on the correct formula and thus the results are not affected by this correction. The errors have not been fixed in the original Article.

Persistence and reversal of plasmid-mediated antibiotic resistance.

In the absence of antibiotic-mediated selection, sensitive bacteria are expected to displace their resistant counterparts if resistance genes are costly. However, many resistance genes persist for long periods in the absence of antibiotics. Horizontal gene transfer (primarily conjugation) could explain this persistence, but it has been suggested that very high conjugation rates would be required. Here, we show that common conjugal plasmids, even when costly, are indeed transferred at sufficiently high rates to be maintained in the absence of antibiotics in Escherichia coli. The notion is applicable to nine plasmids from six major incompatibility groups and mixed populations carrying multiple plasmids. These results suggest that reducing antibiotic use alone is likely insufficient for reversing resistance. Therefore, combining conjugation inhibition and promoting plasmid loss would be an effective strategy to limit conjugation-assisted persistence of antibiotic resistance.

Drug detoxification dynamics explain the postantibiotic effect.

The postantibiotic effect (PAE) refers to the temporary suppression of bacterial growth following transient antibiotic treatment. This effect has been observed for decades for a wide variety of antibiotics and microbial species. However, despite empirical observations, a mechanistic understanding of this phenomenon is lacking. Using a combination of modeling and quantitative experiments, we show that the PAE can be explained by the temporal dynamics of drug detoxification in individual cells after an antibiotic is removed from the extracellular environment. These dynamics are dictated by both the export of the antibiotic and the intracellular titration of the antibiotic by its target. This mechanism is generally applicable for antibiotics with different modes of action. We further show that efflux inhibition is effective against certain antibiotic motifs, which may help explain mixed cotreatment success.

Antibiotics as a selective driver for conjugation dynamics.

It is generally assumed that antibiotics can promote horizontal gene transfer. However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate horizontal gene transfer remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of horizontal gene transfer, serve as a selection force to modulate population dynamics after such gene transfer has occurred, or both. Here, we address this question by quantifying conjugation dynamics in the presence and absence of antibiotic-mediated selection. Surprisingly, we find that sublethal concentrations of antibiotics from the most widely used classes do not significantly increase the conjugation efficiency. Instead, our modelling and experimental results demonstrate that conjugation dynamics are dictated by antibiotic-mediated selection, which can both promote and suppress conjugation dynamics. Our findings suggest that the contribution of antibiotics to the promotion of horizontal gene transfer may have been overestimated. These findings have implications for designing effective antibiotic treatment protocols and for assessing the risks of antibiotic use.

Dynamic control and quantification of bacterial population dynamics in droplets.

Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput. However, difficulties in manipulating the droplet environment and monitoring encapsulated bacterial population for long-term experiments limit its utilization. To overcome these limitations, we used an electrode-free injection technology to modulate the chemical environment in droplets. This ability is critical for precise control of bacterial dynamics in droplets. Moreover, we developed a trapping device for long-term monitoring of population dynamics in individual droplets for at least 240 h. We demonstrated the utility of this new microfluidic system by quantifying population dynamics of natural and engineered bacteria. Our approach can further improve the analysis for systems and synthetic biology in terms of manipulability and high temporal resolution.

Collective antibiotic tolerance: mechanisms, dynamics and intervention.

Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses. Past studies have focused on survival in the presence of antibiotics by individual cells, as genetic mutants or persisters. Also important, however, is the fact that a population of bacterial cells can collectively survive antibiotic treatments lethal to individual cells. This tolerance can arise by diverse mechanisms, including resistance-conferring enzyme production, titration-mediated bistable growth inhibition, swarming and interpopulation interactions. These strategies can enable rapid population recovery after antibiotic treatment and provide a time window during which otherwise susceptible bacteria can acquire inheritable genetic resistance. Here, we emphasize the potential for targeting collective antibiotic tolerance behaviors as an antibacterial treatment strategy.

Emergent dynamics from quorum eavesdropping.

Numerous bacterial species utilize quorum sensing to communicate, but crosstalk often complicates the dynamics of mixed populations. In this issue of Chemistry & Biology, Wu and colleagues take advantage of synthetic gene circuits to elucidate interactions between two quorum sensing systems, with potential applications to fields from infectious diseases to biosynthesis.

Division of labour between Myc and G1 cyclins in cell cycle commitment and pace control.

A body of evidence has shown that the control of E2F transcription factor activity is critical for determining cell cycle entry and cell proliferation. However, an understanding of the precise determinants of this control, including the role of other cell-cycle regulatory activities, has not been clearly defined. Here, recognizing that the contributions of individual regulatory components could be masked by heterogeneity in populations of cells, we model the potential roles of individual components together with the use of an integrated system to follow E2F dynamics at the single-cell level and in real time. These analyses reveal that crossing a threshold amplitude of E2F accumulation determines cell cycle commitment. Importantly, we find that Myc is critical in modulating the amplitude, whereas cyclin D/E activities have little effect on amplitude but do contribute to the modulation of duration of E2F activation, thereby affecting the pace of cell cycle progression.

Linear population allocation by bistable switches in response to transient stimulation.

Many cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g. OFF and ON). While extensive studies have been carried out to analyze various bistable networks, they are primarily focused on responses of bistable networks to sustained input signals. In this work, we investigate the response characteristics of bistable networks to transient signals, using both theoretical analysis and numerical simulation. We find that bistable systems exhibit a common property: for input signals with short durations, the fraction of switching cells increases linearly with the signal duration, allowing the population to integrate transient signals to tune its response. We propose that this allocation algorithm can be an optimal response strategy for certain cellular decisions in which excessive switching results in lower population fitness.

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival.

Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms.

Generic metric to quantify quorum sensing activation dynamics.

Quorum sensing (QS) enables bacteria to sense and respond to changes in their population density. It plays a critical role in controlling different biological functions, including bioluminescence and bacterial virulence. It has also been widely adapted to program robust dynamics in one or multiple cellular populations. While QS systems across bacteria all appear to function similarly-as density-dependent control systems-there is tremendous diversity among these systems in terms of signaling components and network architectures. This diversity hampers efforts to quantify the general control properties of QS. For a specific QS module, it remains unclear how to most effectively characterize its regulatory properties in a manner that allows quantitative predictions of the activation dynamics of the target gene. Using simple kinetic models, here we show that the dominant temporal dynamics of QS-controlled target activation can be captured by a generic metric, 'sensing potential', defined at a single time point. We validate these predictions using synthetic QS circuits in Escherichia coli. Our work provides a computational framework and experimental methodology to characterize diverse natural QS systems and provides a concise yet quantitative criterion for selecting or optimizing a QS system for synthetic biology applications.

The inoculum effect and band-pass bacterial response to periodic antibiotic treatment.

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

A distributed system for fast alignment of next-generation sequencing data.

We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.