Design and Discovery of a Potent and Selective Inhibitor of Integrin αvß1.

Selective inhibition of the RGD (Arg-Gly-Asp) integrin αvß1 has been recently identified as an attractive therapeutic approach for the treatment of liver fibrosis given its function, target expression, and safety profile. Our identification of a non-RGD small molecule lead followed by focused, systematic changes to the core structure utilizing a crystal structure, in silico modeling, and a tractable synthetic approach resulted in the identification of a potent small molecule exhibiting a remarkable affinity for αvß1 relative to several other integrin isoforms measured. Azabenzimidazolone 25 demonstrated antifibrotic efficacy in an in vivo rat liver fibrosis model and represents a tool compound capable of further exploring the biological consequences of selective αvß1 inhibition.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development.

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

Current practices for QSP model assessment: an IQ consortium survey.

Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.

Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making.

The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen).

Contribution of Extrahepatic Aldehyde Oxidase Activity to Human Clearance.

Aldehyde oxidase (AOX) is a soluble, cytosolic enzyme that metabolizes various N-heterocyclic compounds and organic aldehydes. It has wide tissue distribution with highest levels found in liver, kidney, and lung. Human clearance projections of AOX substrates by in vitro assessments in isolated liver fractions (cytosol, S9) and even hepatocytes have been largely underpredictive of clinical outcomes. Various hypotheses have been suggested as to why this is the case. One explanation is that extrahepatic AOX expression contributes measurably to AOX clearance and is at least partially responsible for the often observed underpredictions. Although AOX expression has been confirmed in several extrahepatic tissues, activities therein and potential contribution to overall human clearance have not been thoroughly studied. In this work, the AOX enzyme activity using the S9 fractions of select extrahepatic human tissues (kidney, lung, vasculature, and intestine) were measured using carbazeran as a probe substrate. Measured activities were scaled to a whole-body clearance using best-available parameters and compared with liver S9 fractions. Here, the combined scaled AOX clearance obtained from the kidney, lung, vasculature, and intestine is very low and amounted to <1% of liver. This work suggests that AOX metabolism from extrahepatic sources plays little role in the underprediction of activity in human. One of the notable outcomes of this work has been the first direct demonstration of AOX activity in human vasculature. SIGNIFICANCE STATEMENT: This work demonstrates aldehyde oxidase (AOX) activity is measurable in a variety of extrahepatic human tissues, including vasculature, yet activities and potential contributions to human clearance are relatively low and insignificant when compared with the liver. Additionally, the modeling of the tissue-specific in vitro kinetic data suggests that AOX may be influenced by the tissue it resides in and thus show different affinity, activity, and modified activity over time.

A quantitative systems pharmacology model of colonic motility with applications in drug development.

We developed a mathematical model of colon physiology driven by serotonin signaling in the enteric nervous system. No such models are currently available to assist drug discovery and development for GI motility disorders. Model parameterization was informed by published preclinical and clinical data. Our simulations provide clinically relevant readouts of bowel movement frequency and stool consistency. The model recapitulates healthy and slow transit constipation phenotypes, and the effect of a 5-HT4 receptor agonist in healthy volunteers. Using the calibrated model, we predicted the agonist dose to normalize defecation frequency in slow transit constipation while avoiding the onset of diarrhea. Model sensitivity analysis predicted that changes in HAPC frequency and liquid secretion have the greatest impact on colonic motility. However, exclusively increasing the liquid secretion can lead to diarrhea. In contrast, increasing HAPC frequency alone can enhance bowel frequency without leading to diarrhea. The quantitative systems pharmacology approach used here demonstrates how mechanistic modeling of disease pathophysiology expands our understanding of biology and supports judicious hypothesis generation for therapeutic intervention.

Synchronized cycles of bacterial lysis for in vivo delivery.

The widespread view of bacteria as strictly pathogenic has given way to an appreciation of the prevalence of some beneficial microbes within the human body. It is perhaps inevitable that some bacteria would evolve to preferentially grow in environments that harbor disease and thus provide a natural platform for the development of engineered therapies. Such therapies could benefit from bacteria that are programmed to limit bacterial growth while continually producing and releasing cytotoxic agents in situ. Here we engineer a clinically relevant bacterium to lyse synchronously ata threshold population density and to release genetically encoded cargo. Following quorum lysis, a small number of surviving bacteria reseed the growing population, thus leading to pulsatile delivery cycles. We used microfluidic devices to characterize the engineered lysis strain and we demonstrate its potential as a drug delivery platform via co-culture with human cancer cells in vitro. Asa proof of principle, we tracked the bacterial population dynamics in ectopic syngeneic colorectal tumours in mice via a luminescent reporter. The lysis strain exhibits pulsatile population dynamics in vivo, with mean bacterial luminescence that remained two orders of magnitude lower than an unmodified strain. Finally, guided by previous findings that certain bacteria can enhance the efficacy of standard therapies, we orally administered the lysis strain alone or in combination with a clinical chemotherapeutic to a syngeneic mouse transplantation model of hepatic colorectal metastases. We found that the combination of both circuit-engineered bacteria and chemotherapy leads to a notable reduction of tumour activity along with a marked survival benefit over either therapy alone.Our approach establishes a methodology for leveraging the tools of synthetic biology to exploit the natural propensity for certain bacteria to colonize disease sites.

Evaluation of a Mathematical Model of Rat Body Weight Regulation in Application to Caloric Restriction and Drug Treatment Studies.

The purpose of this work is to develop a mathematical model of energy balance and body weight regulation that can predict species-specific response to common pre-clinical interventions. To this end, we evaluate the ability of a previously published mathematical model of mouse metabolism to describe changes in body weight and body composition in rats in response to two short-term interventions. First, we adapt the model to describe body weight and composition changes in Sprague-Dawley rats by fitting to data previously collected from a 26-day caloric restriction study. The calibrated model is subsequently used to describe changes in rat body weight and composition in a 23-day cannabinoid receptor 1 antagonist (CB1Ra) study. While the model describes body weight data well, it fails to replicate body composition changes with CB1Ra treatment. Evaluation of a key model assumption about deposition of fat and fat-free masses shows a limitation of the model in short-term studies due to the constraint placed on the relative change in body composition components. We demonstrate that the model can be modified to overcome this limitation, and propose additional measurements to further test the proposed model predictions. These findings illustrate how mathematical models can be used to support drug discovery and development by identifying key knowledge gaps and aiding in the design of additional experiments to further our understanding of disease-relevant and species-specific physiology.

A Microfluidic Platform for Long-Term Monitoring of Algae in a Dynamic Environment.

Culturing cells in microfluidic "lab-on-a-chip" devices for time lapse microscopy has become a valuable tool for studying the dynamics of biological systems. Although microfluidic technology has been applied to culturing and monitoring a diverse range of bacterial and eukaryotic species, cyanobacteria and eukaryotic microalgae present several challenges that have made them difficult to culture in a microfluidic setting. Here, we present a customizable device for the long-term culturing and imaging of three well characterized strains of cyanobacteria and microalgae. This platform has several advantages over agarose pads and demonstrates great potential for obtaining high quality, single-cell gene expression data of cyanobacteria and algae in precisely controlled, dynamic environments over long time periods.

Self-reproducing catalyst drives repeated phospholipid synthesis and membrane growth.

Cell membranes are dynamic structures found in all living organisms. There have been numerous constructs that model phospholipid membranes. However, unlike natural membranes, these biomimetic systems cannot sustain growth owing to an inability to replenish phospholipid-synthesizing catalysts. Here we report on the design and synthesis of artificial membranes embedded with synthetic, self-reproducing catalysts capable of perpetuating phospholipid bilayer formation. Replacing the complex biochemical pathways used in nature with an autocatalyst that also drives lipid synthesis leads to the continual formation of triazole phospholipids and membrane-bound oligotriazole catalysts from simpler starting materials. In addition to continual phospholipid synthesis and vesicle growth, the synthetic membranes are capable of remodeling their physical composition in response to changes in the environment by preferentially incorporating specific precursors. These results demonstrate that complex membranes capable of indefinite self-synthesis can emerge when supplied with simpler chemical building blocks.

Systems biology. Accurate information transmission through dynamic biochemical signaling networks.

Stochasticity inherent to biochemical reactions (intrinsic noise) and variability in cellular states (extrinsic noise) degrade information transmitted through signaling networks. We analyzed the ability of temporal signal modulation--that is, dynamics--to reduce noise-induced information loss. In the extracellular signal-regulated kinase (ERK), calcium (Ca(2+)), and nuclear factor kappa-B (NF-κB) pathways, response dynamics resulted in significantly greater information transmission capacities compared to nondynamic responses. Theoretical analysis demonstrated that signaling dynamics has a key role in overcoming extrinsic noise. Experimental measurements of information transmission in the ERK network under varying signal-to-noise levels confirmed our predictions and showed that signaling dynamics mitigate, and can potentially eliminate, extrinsic noise-induced information loss. By curbing the information-degrading effects of cell-to-cell variability, dynamic responses substantially increase the accuracy of biochemical signaling networks.

Dynamic localization of the cyanobacterial circadian clock proteins.

BACKGROUND: The cyanobacterial circadian clock system has been extensively studied, and the structures, interactions, and biochemical activities of the central oscillator proteins (KaiA, KaiB, and KaiC) have been well elucidated. Despite this rich repository of information, little is known about the distribution of these proteins within the cell. RESULTS: Here we report that KaiA and KaiC localize as discrete foci near a single pole of cells in a clock-dependent fashion, with enhanced polar localization observed at night. KaiA localization is dependent on KaiC; consistent with this notion, KaiA and KaiC colocalize with each other, as well as with CikA, a key input and output factor previously reported to display unipolar localization. The molecular mechanism that localizes KaiC to the poles is conserved in Escherichia coli, another Gram-negative rod-shaped bacterium, suggesting that KaiC localization is not dependent on other clock- or cyanobacterial-specific factors. Moreover, expression of CikA mutant variants that distribute diffusely results in the striking delocalization of KaiC. CONCLUSIONS: This work shows that the cyanobacterial circadian system undergoes a circadian orchestration of subcellular organization. We propose that the observed spatiotemporal localization pattern represents a novel layer of regulation that contributes to the robustness of the clock by facilitating protein complex formation and synchronizing the clock with environmental stimuli.

Rapid and tunable post-translational coupling of genetic circuits.

One promise of synthetic biology is the creation of genetic circuitry that enables the execution of logical programming in living cells. Such 'wet programming' is positioned to transform a wide and diverse swathe of biotechnology ranging from therapeutics and diagnostics to water treatment strategies. Although progress in the development of a library of genetic modules continues apace, a major challenge for their integration into larger circuits is the generation of sufficiently fast and precise communication between modules. An attractive approach is to integrate engineered circuits with host processes that facilitate robust cellular signalling. In this context, recent studies have demonstrated that bacterial protein degradation can trigger a precise response to stress by overloading a limited supply of intracellular proteases. Here we use protease competition to engineer rapid and tunable coupling of genetic circuits across multiple spatial and temporal scales. We characterize coupling delay times that are more than an order of magnitude faster than standard transcription-factor-based coupling methods (less than 1 min compared with ∼20-40 min) and demonstrate tunability through manipulation of the linker between the protein and its degradation tag. We use this mechanism as a platform to couple genetic clocks at the intracellular and colony level, then synchronize the multi-colony dynamics to reduce variability in both clocks. We show how the coupled clock network can be used to encode independent environmental inputs into a single time series output, thus enabling frequency multiplexing (information transmitted on a common channel by distinct frequencies) in a genetic circuit context. Our results establish a general framework for the rapid and tunable coupling of genetic circuits through the use of native 'queueing' processes such as competitive protein degradation.

Dual delayed feedback provides sensitivity and robustness to the NF-κB signaling module.

Many cellular stress-responsive signaling systems exhibit highly dynamic behavior with oscillatory features mediated by delayed negative feedback loops. What remains unclear is whether oscillatory behavior is the basis for a signaling code based on frequency modulation (FM) or whether the negative feedback control modules have evolved to fulfill other functional requirements. Here, we use experimentally calibrated computational models to interrogate the negative feedback loops that regulate the dynamic activity of the transcription factor NF-κB. Linear stability analysis of the model shows that oscillatory frequency is a hard-wired feature of the primary negative feedback loop and not a function of the stimulus, thus arguing against an FM signaling code. Instead, our modeling studies suggest that the two feedback loops may be tuned to provide for rapid activation and inactivation capabilities for transient input signals of a wide range of durations; by minimizing late phase oscillations response durations may be fine-tuned in a graded rather than quantized manner. Further, in the presence of molecular noise the dual delayed negative feedback system minimizes stochastic excursions of the output to produce a robust NF-κB response.

Genetic Circuits in Salmonella typhimurium.

Synthetic biology has rapidly progressed over the past decade and is now positioned to impact important problems in health and energy. In the clinical arena, the field has thus far focused primarily on the use of bacteria and bacteriophages to overexpress therapeutic gene products. The next generation of multigene circuits will control the triggering, amplitude, and duration of therapeutic activity in vivo. This will require a host organism that is easy to genetically modify, leverages existing successful circuit designs, and has the potential for use in humans. Here, we show that gene circuits that were originally constructed and tested in Escherichia coli translate to Salmonella typhimurium, a therapeutically relevant microbe with attenuated strains that have exhibited safety in several human clinical trials. These strains are essentially nonvirulent, easy to genetically program, and specifically grow in tumor environments. Developing gene circuits on this platform could enhance our ability to bring sophisticated genetic programming to cancer therapy, setting the stage for a new generation of synthetic biology in clinically relevant microbes.

Recent advances in single-cell studies of gene regulation.

A mechanistic understanding of gene regulatory network dynamics requires quantitative single-cell data of multiple network components in response to well-defined perturbations. Recent advances in the development of fluorescent biomarkers for proteins, detection of RNA and interactions, microfluidic technology, and high-resolution imaging have set the stage for a host of new studies that elucidate the important roles of stochasticity and cell-cell variability in response to external perturbations. In this review, we briefly describe methods for high-resolution visualization and the control of gene expression, along with application of these novel methods to recent studies involving gene networks.

Entrainment of a population of synthetic genetic oscillators.

Biological clocks are self-sustained oscillators that adjust their phase to the daily environmental cycles in a process known as entrainment. Molecular dissection and mathematical modeling of biological oscillators have progressed quite far, but quantitative insights on the entrainment of clocks are relatively sparse. We simultaneously tracked the phases of hundreds of synthetic genetic oscillators relative to a common external stimulus to map the entrainment regions predicted by a detailed model of the clock. Synthetic oscillators were frequency-locked in wide intervals of the external period and showed higher-order resonance. Computational simulations indicated that natural oscillators may contain a positive-feedback loop to robustly adapt to environmental cycles.

Optimal movement in the prey strikes of weakly electric fish: a case study of the interplay of body plan and movement capability.

Animal behaviour arises through a complex mixture of biomechanical, neuronal, sensory and control constraints. By focusing on a simple, stereotyped movement, the prey capture strike of a weakly electric fish, we show that the trajectory of a strike is one which minimizes effort. Specifically, we model the fish as a rigid ellipsoid moving through a fluid with no viscosity, governed by Kirchhoff's equations. This formulation allows us to exploit methods of discrete mechanics and optimal control to compute idealized fish trajectories that minimize a cost function. We compare these with the measured prey capture strikes of weakly electric fish from a previous study. The fish has certain movement limitations that are not incorporated in the mathematical model, such as not being able to move sideways. Nonetheless, we show quantitatively that the computed least-cost trajectories are remarkably similar to the measured trajectories. Since, in this simplified model, the basic geometry of the idealized fish determines the favourable modes of movement, this suggests a high degree of influence between body shape and movement capability. Simplified minimal models and optimization methods can give significant insight into how body morphology and movement capability are closely attuned in fish locomotion.