Effective drug combination for Caenorhabditis elegans nematodes discovered by output-driven feedback system control technique

An automated, high-resolution phenotypic assay for adult Brugia malayi and microfilaria

Brugia malayi are thread-like parasitic worms and one of the etiological agents of Lymphatic filariasis (LF). Existing anthelmintic drugs to treat LF are effective in reducing the larval microfilaria (mf) counts in human bloodstream but are less effective on adult parasites. To test potential drug candidates, we report a multi-parameter phenotypic assay based on tracking the motility of adult B. malayi and mf in vitro. For adult B. malayi, motility is characterized by the centroid velocity, path curvature, angular velocity, eccentricity, extent, and Euler Number. These parameters are evaluated in experiments with three anthelmintic drugs. For B. malayi mf, motility is extracted from the evolving body skeleton to yield positional data and bending angles at 74 key point. We achieved high-fidelity tracking of complex worm postures (self-occlusions, omega turns, body bending, and reversals) while providing a visual representation of pose estimates and behavioral attributes in both space and time scales.

Compound combinations targeting longevity: Challenges and perspectives

Aging is one of the world's greatest concerns, requiring urgent, effective, large-scale interventions to decrease the number of late-life chronic diseases and improve human healthspan. Anti-aging drug therapy is one of the most promising strategies to combat the effects of aging. However, most geroprotective compounds are known to successfully affect only a few aging-related targets. Given this, there is a great biological rationale for the use of combinations of anti-aging interventions. In this review, we characterize the various types of compound combinations used to modulate lifespan, discuss the existing evidence on their role in life extension, and present some key points about current challenges and future prospects for the development of combination drug anti-aging therapy.

Caenorhabditis elegans as a valuable model for the study of anthelmintic pharmacodynamics and drug-drug interactions: The case of ivermectin and eprinomectin

Caenorhabditis elegans is a free-living nematode that has been validated for anthelmintic drug screening. However, this model has not been used to address anthelmintic dose-response-time and drug-drug interactions through matrix array methodology. Eprinomectin (EPM) and Ivermectin (IVM) are macrocyclic lactones widely used as anthelmintics. Despite being very similar, EPM and IVM are combined in commercial formulations or mixed by farmers, under the assumption that the combination would increase their efficacy. However, there is no data reported on the pharmacological evaluation of the combination of both drugs. In this study, we assessed the pharmacodynamics and drug-drug interactions of these two anthelmintic drugs. Since the action of these drugs causes worm paralysis, we used an infrared motility assay to measure EPM and IVM effects on worm movement over time. The results showed that EPM was slightly more potent than IVM, that drug potency increased with drug time exposure, and that once paralyzed, worms did not recover. Different EPM/IVM concentration ratios were used and synergy and combination sensitivity scores were determined at different exposure times, applying Highest Single Agent (HSA), Loewe additivity, Bliss and Zero Interaction Potency (ZIP) models. The results clearly indicate that there is neither synergy nor antagonism between both macrocyclic lactones. This study shows that it is more relevant to prioritize the exposure time of each individual drug than to combine them to improve their effects. The results highlight the utility of C. elegans to address pharmacodynamics studies, particularly for drug-drug interactions. Models in vitro can be integrated to facilitate preclinical and clinical translational studies and help researchers to understand drug-drug interactions and achieve rational therapeutic regimes.

Synergistic antitumor effects of compound-composed optimal formula from Aidi injection on hepatocellular carcinoma and colorectal cancer

BACKGROUND

Traditional Chinese medicine formula (TCMF) possesses unique advantages in the prevention and treatment of malignant tumors such as hepatocellular carcinoma (HCC) and colorectal cancer (CRC). However, the unclear chemical composition and mechanism lead to its unstable efficacy and adverse reactions occurring frequently, especially injection. We previously proposed the research idea and strategy for compound-composed Chinese medicine formula (CCMF).

PURPOSE

A demonstration study was performed through screening of the compound-composed optimal formula (COF) from Aidi injection, confirmation of the synergistic effect, and exploration of the related mechanism in the treatment of HCC and CRC.

METHOD

The feedback system control (FSC) technique was applied to screening of COF. CCK-8 and calcein-AM/PI assays were performed to evaluate cell proliferation. Cell apoptosis was assessed using flow cytometry and DAPI staining. JC-1 probe and mitochondrial staining were employed to detect mitochondrial membrane potential (MMP) and the release of cytochrome c into cytoplasm, respective. Quantitative proteomics, drug affinity responsive target stability (DARTS) assay, bioinformatics, and molecular docking were carried out to explore the targets of the compounds and the synergistic mechanism involved.

RESULTS

COF was obtained from Aidi injection, which comprises cantharidin (CAN): calycosin-7-O-β-D-glucoside (CAG): ginsenoside Rc: ginsenoside Rd = 1:12:12:8 (molar ratio). The monarch drug CAN in combination with minister medicines consisting of CAG, Rc and Rd (abbr. TD) displayed evidently synergistic effect, which inhibited cell viability, increased dead cell number, induced apoptosis, reduced MMP, promoted cytochrome c leakage of HCC and CRC cells, and suppressed the increases of tumor volume and weight in HCC and CRC bearing nude mice. TD probably antagonized CAN enhanced activity of the ubiquitin proteasome system (UPS) to depress the degradation of cytotoxic proteins through binding to ubiquitin proteasome, thus exerting the synergistic effect with CAN activated protein phosphatase 2A (PP2A) to activate the mitochondrial apoptosis pathway. In addition, the CAN enhanced protein expression of UPS was also observed for the first time.

CONCLUSION

CAN and TD exert synergism through activation of PP2A and inhibition of UPS. It makes sense to elucidate the scientific nature of the compatibility theory of TCMF based on CCMF, which will be an important research direction of the modernization of traditional Chinese medicines.

Whole-organism phenotypic screening methods used in early-phase anthelmintic drug discovery

Diseases caused by parasitic helminths (worms) represent a major global health burden in both humans and animals. As vaccines against helminths have yet to achieve a prominent role in worm control, anthelmintics are the primary tool to limit production losses and disease due to helminth infections in both human and veterinary medicine. However, the excessive and often uncontrolled use of these drugs has led to widespread anthelmintic resistance in these worms - particularly of animals - to almost all commercially available anthelmintics, severely compromising control. Thus, there is a major demand for the discovery and development of new classes of anthelmintics. A key component of the discovery process is screening libraries of compounds for anthelmintic activity. Given the need for, and major interest by the pharmaceutical industry in, novel anthelmintics, we considered it both timely and appropriate to re-examine screening methods used for anthelmintic discovery. Thus, we reviewed current literature (1977-2021) on whole-worm phenotypic screening assays developed and used in academic laboratories, with a particular focus on those employed to discover nematocides. This review reveals that at least 50 distinct phenotypic assays with low-, medium- or high-throughput capacity were developed over this period, with more recently developed methods being quantitative, semi-automated and higher throughput. The main features assessed or measured in these assays include worm motility, growth/development, morphological changes, viability/lethality, pharyngeal pumping, egg hatching, larval migration, CO₂- or ATP-production and/or enzyme activity. Recent progress in assay development has led to the routine application of practical, cost-effective, medium- to high-throughput whole-worm screening assays in academic or public-private partnership (PPP) contexts, and major potential for novel high-content, high-throughput platforms in the near future. Complementing this progress are major advances in the molecular data sciences, computational biology and informatics, which are likely to further enable and accelerate anthelmintic drug discovery and development.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Concentration optimization of combinatorial drugs using Markov chain-based models

BACKGROUND

Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.

RESULTS

In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.

CONCLUSION

This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

Comparison of electrophysiological and motility assays to study anthelmintic effects in Caenorhabditis elegans

Currently, only a few chemical drug classes are available to control the global burden of nematode infections in humans and animals. Most of these drugs exert their anthelmintic activity by interacting with proteins such as ion channels, and the nematode neuromuscular system remains a promising target for novel intervention strategies. Many commonly-used phenotypic readouts such as motility provide only indirect insight into neuromuscular function and the site(s) of action of chemical compounds. Electrophysiological recordings provide more specific information but are typically technically challenging and lack high throughput for drug discovery. Because drug discovery relies strongly on the evaluation and ranking of drug candidates, including closely related chemical derivatives, precise assays and assay combinations are needed for capturing and distinguishing subtle drug effects.

Past studies show that nematode motility and pharyngeal pumping (feeding) are inhibited by most anthelmintic drugs. Here we compare two microfluidic devices (“chips”) that record electrophysiological signals from the nematode pharynx (electropharyngeograms; EPGs) ─ the ScreenChip™ and the 8-channel EPG platform ─ to evaluate their respective utility for anthelmintic research. We additionally compared EPG data with whole-worm motility measurements obtained with the wMicroTracker instrument. As references, we used three macrocyclic lactones (ivermectin, moxidectin, and milbemycin oxime), and levamisole, which act on different ion channels. Drug potencies (IC₅₀ and IC₉₅ values) from concentration-response curves, and the time-course of drug effects, were compared across platforms and across drugs. Drug effects on pump timing and EPG waveforms were also investigated. These experiments confirmed drug-class specific effects of the tested anthelmintics and illustrated the relative strengths and limitations of the different assays for anthelmintic research.

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Anthelmintic drugs inhibit pharyngeal pumping and motility in C. elegans.

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Two electrophysiological assays and one motility assay were compared.

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Macrocyclic lactones and levamisole have drug-class-specific effects.

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A combination of assays most fully reveals anthelmintic effects.

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Strengths and limitations of the three assays were identified.

Withanone Ameliorates Stress Symptoms in Caenorhabditis Elegans by Acting through Serotonin Receptors

INTRODUCTION

Depression is responsible for 800 000 deaths worldwide, a number that will rise significantly due to the COVID-19 pandemic. Affordable novel drugs with less severe side effects are urgently required. We investigated the effect of withanone (WN) from Withania somnifera on the serotonin system of wild-type and knockout Caenorhabditis elegans strains using in silico, in vitro, and in vivo methods.

METHODS

WN or fluoxetine (as positive control drug) was administered to wild-type (N2) and knockout C. elegans strains (AQ866, DA1814, DA2100, DA2109, and MT9772) to determine their effect on oxidative stress (Trolox, H₂DCFDA, and juglone assays) on osmotic stress and heat stress and lifespan. Quantitative real-time RT-PCR was applied to investigate the effect of WN or fluoxetine on the expression of serotonin receptors (ser-1, ser-4, ser-7) and serotonin transporter (mod-5). The binding affinity of WN to serotonin receptors and transporter was analyzed in silico using AutoDock 4.2.6.

RESULTS

WN scavenged ROS in wild-type and knockout C. elegans and prolonged their lifespan. WN upregulated the expression of serotonin receptor and transporter genes. In silico analyses revealed high binding affinities of WN to Ser-1, Ser-4, Ser-7, and Mod-5.

LIMITATIONS

Further studies are needed to prove whether the results from C. elegans are transferrable to mammals and human beings.

CONCLUSION

WN ameliorated depressive-associated stress symptoms by activating the serotonin system. WN may serve as potential candidate in developing new drugs to treat depression.

SKN-1 is involved in combination of apple peels and blueberry extracts synergistically protecting against oxidative stress in Caenorhabditis elegans

Increased consumption of fruits and vegetables is associated with reduced risk of age-related functional declines and chronic diseases, primarily attributed to their bioactive phytochemicals. Apples and blueberries are rich in phytochemicals with a wide range of biological activities and health benefits. Our previous research has shown the combination of apple peel extracts (APE) and blueberry extracts (BE) can synergistically promote the lifespan of Caenorhabditis elegans (C. elegans). The objectives of this study were to determine whether the extension of lifespan was involved in regulation of oxidative stress, and to explore the underlying mechanisms of action. The results showed that the combination of APE and BE could synergistically ameliorate oxidative stress by improving antioxidant enzyme activities and enhancing resistance to paraquat. Meanwhile, treatment with APE plus BE could down-regulate the overexpression of reactive oxygen species (ROS) and affect the expression of antioxidant related genes, including sod-3, cat-1, ctl-1, skn-1, mev-1 and isp-1. However, administration with APE plus BE abolished the extension of the lifespan of skn-1(zu135) mutants, and inhibited the expression of skn-1 downstream genes, including gcs-1, gst-4 and gst-7. In addition, supplementation with APE plus BE could promote the migration of SKN-1 into the nucleus, which eliminated improvement to ROS and paraquat. In conclusion, the combination of APE and BE could synergistically protect against oxidative stress in C. elegans via the SKN-1/Nrf2 pathway. This study provided the theoretical basis to explore the combination of phytochemicals in the prevention of aging regulated by oxidative stress.

Caenorhabditis elegans in anthelmintic research - Old model, new perspectives

For more than four decades, the free-living nematode Caenorhabditis elegans has been extensively used in anthelmintic research. Classic genetic screens and heterologous expression in the C. elegans model enormously contributed to the identification and characterization of molecular targets of all major anthelmintic drug classes. Although these findings provided substantial insights into common anthelmintic mechanisms, a breakthrough in the treatment and control of parasitic nematodes is still not in sight. Instead, we are facing increasing evidence that the enormous diversity within the phylum Nematoda cannot be recapitulated by any single free-living or parasitic species and the development of novel broad-spectrum anthelmintics is not be a simple goal. In the present review, we summarize certain milestones and challenges of the C. elegans model with focus on drug target identification, anthelmintic drug discovery and identification of resistance mechanisms. Furthermore, we present new perspectives and strategies on how current progress in C. elegans research will support future anthelmintic research.

Harnessing a Novel Machine-Learning-Assisted Evolutionary Algorithm to Co-optimize Three Characteristics of an Electrospun Oil Sorbent

The optimization of materials is challenging as it often involves simultaneous manipulation of an assembly of condition parameters, which generates an enormous combinational space. Thus, optimization models and algorithms are widely adopted to accelerate material design and optimization. However, most optimization strategies can poorly handle multiple parameters simultaneously with limited prior knowledge. Herein, we describe a novel systematic optimization strategy, namely, machine-learning-assisted differential evolution, which combines machine learning and the evolutionary algorithm together, for zero-prior-data, rapid, and simultaneous optimization of multiple objectives. The strategy enables the evolutionary algorithm to "learn" so as to accelerate the optimization process, and also to identify quantitative interactions between the condition parameters and functional characteristics of the material. The performance of the strategy is verified by in silico simulations, as well as an application on simultaneously optimizing three characteristics, namely, water contact angle, oil absorption capacity, and mechanical strength, of an electrospun polystyrene/polyacrylonitrile (PS/PAN) material as a potential sorbent for a marine oil spill. With only 50 tests, the optimal fabrication parameters were successfully located from a combinatorial space of 50 000 possibilities. The presented platform technique offers a universal enabling technology to identify the optimal conditions rapidly from a daunting parameter space to synthesize materials with multiple desired functionalities.

Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention

In 2019/2020, the emergence of coronavirus disease 2019 (COVID-19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID-19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi-drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose-finding to achieve drug synergy. This approach is widely-used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI-based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top-ranked combination being optimally and sub-optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism-agnostic, and potentially applicable to the systematic N-of-1 and population-wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID-19 and future pandemics.

Compound-based Chinese medicine formula: From discovery to compatibility mechanism

ETHNOPHARMACOLOGICAL RELEVANCE

Chinese medicine formula (CMF) has a long history of clinical use in the treatment of various diseases under the guidance of traditional Chinese medicine (TCM) theory. The application of CMF can be divided into three levels, crude extracts, homologous compounds mixture, and specific compounds. However, the modern scientific connotation of the CMF theory has not been clarified.

AIM OF THE REVIEW

To critically evaluate the research strategy for the investigation of compound-based CMF (CCMF).

MATERIALS AND METHODS

The related information was collected from the scientific databases, including CNKI, Elsevier, ScienceDirect, PubMed, SpringerLink, Web of Science, and Wiley Online.

RESULTS

The research design including discovery, screening, optimization, pharmacodynamics models, and target research techniques including the targets for compatibility compounds were evaluated. Essentially it has been evaluated that the in vitro multicellular three-dimensional culture or organoid model has been proposed for the optimization model for compatibility research of CCMF. Based on these, the traditional compatibility theory of CMF, such as Monarch-Minister-Assistant-Guide (Jun-Chen-Zuo-Shi in Chinese), can probably be elucidated by the CCMF research.

CONCLUSIONS

CCMF has the clear advantage of providing the exact composition and controllable quality of modern medicines, in addition to having the characteristics of multi-ingredients and multi-targets synergistic effects of TCM. However, CCMF is still associated with challenges which need to be addressed for its future use.

Enabling Technologies for Personalized and Precision Medicine

Individualizing patient treatment is a core objective of the medical field. Reaching this objective has been elusive owing to the complex set of factors contributing to both disease and health; many factors, from genes to proteins, remain unknown in their role in human physiology. Accurately diagnosing, monitoring, and treating disorders requires advances in biomarker discovery, the subsequent development of accurate signatures that correspond with dynamic disease states, as well as therapeutic interventions that can be continuously optimized and modulated for dose and drug selection. This work highlights key breakthroughs in the development of enabling technologies that further the goal of personalized and precision medicine, and remaining challenges that, when addressed, may forge unprecedented capabilities in realizing truly individualized patient care.

Drug discovery technologies: Caenorhabditis elegans as a model for anthelmintic therapeutics

Helminthiasis is one of the gravest problems worldwide. There is a growing concern on less available anthelmintics and the emergence of resistance creating a major threat to human and livestock health resources. Novel and broad-spectrum anthelmintics are urgently needed. The free-living nematode Caenorhabditis elegans could address this issue through automated high-throughput technologies for the screening of large chemical libraries. This review discusses the strong advantages and limitations for using C elegans as a screening method for anthelmintic drug discovery. C elegans is the best model available for the validation of novel effective drugs in treating most, if not all, helminth infections, and for the elucidation the mode of action of anthelmintic candidates. This review also focuses on available technologies in the discovery of anthelmintics published over the last 15 years with particular attention to high-throughput technologies over conventional screens. On the other hand, this review highlights how combinatorial and nanomedicine strategies could prolong the use of anthelmintics and control resistance problems.

Raspberry extract promoted longevity and stress toleranceviathe insulin/IGF signaling pathway and DAF-16 inCaenorhabditis elegans

Increased consumption of fruits and vegetables is associated with a reduced risk of age-related functional decline and chronic diseases, which is primarily attributed to phytochemicals.

Combination of apple peel and blueberry extracts synergistically induced lifespan extension via DAF-16 in Caenorhabditis elegans

Apples and blueberries are rich in phytochemicals with a wide range of biological activities and health benefits. Our research found that the combination of apple peel extracts and blueberry extracts could synergistically promote the lifespan via DAF-16 in C. elegans.

Microfluidic Technologies for High Throughput Screening Through Sorting and On-Chip Culture of C. elegans

The nematode Caenorhabditis elegans is a powerful model organism that has been widely used to study molecular biology, cell development, neurobiology, and aging. Despite their use for the past several decades, the conventional techniques for growth, imaging, and behavioral analysis of C. elegans can be cumbersome, and acquiring large data sets in a high-throughput manner can be challenging. Developments in microfluidic “lab-on-a-chip” technologies have improved studies of C. elegans by increasing experimental control and throughput. Microfluidic features such as on-chip control layers, immobilization channels, and chamber arrays have been incorporated to develop increasingly complex platforms that make experimental techniques more powerful. Genetic and chemical screens are performed on C. elegans to determine gene function and phenotypic outcomes of perturbations, to test the effect that chemicals have on health and behavior, and to find drug candidates. In this review, we will discuss microfluidic technologies that have been used to increase the throughput of genetic and chemical screens in C. elegans. We will discuss screens for neurobiology, aging, development, behavior, and many other biological processes. We will also discuss robotic technologies that assist in microfluidic screens, as well as alternate platforms that perform functions similar to microfluidics.

Adhesive Tape Microfluidics with an Autofocusing Module That Incorporates CRISPR Interference: Applications to Long-Term Bacterial Antibiotic Studies

The ability to study bacteria at the single cell level has advanced our insights into microbial physiology and genetics in ways not attainable by studying large populations using more traditional culturing methods. To improve methods to characterize bacteria at the cellular level, we developed a new microfluidic platform that enables cells to be exposed to metabolites in a gradient of concentrations. By designing low-cost, three-dimensional devices with adhesive tapes and tailoring them for bacterial imaging, we avoided the complexities of silicon and polymeric microfabrication. The incorporation of an agarose membrane as the resting substrate, along with a temperature-controlled environmental chamber, allows the culturing of bacterial cells for over 10 h under stable growth or inhibition conditions. Incorporation of an autofocusing module helped the uninterrupted, high-resolution observation of bacteria at the single-cell and at low density population levels. We used the microfluidic platform to record morphological changes in Escherichia coli during ampicillin exposure and to quantify the minimum inhibitory concentration of the antibiotic. We further demonstrated the potential of finely-tuned, incremental gene regulation in a concentration gradient utilizing CRISPR interference (CRISPRi). These low-cost engineering tools, when implemented in combination with genetic approaches such as CRISPRi, should prove useful to uncover new genetic determinants of antibiotic susceptibility and evaluate the long-term effectiveness of antibiotics in bacterial cultures.

AI‐Enabled Parabolic Response Surface Approach Identifies Ultra Short‐Course Near‐Universal TB Drug Regimens

Tuberculosis (TB) is a major health problem that causes more deaths worldwide than any other single infectious disease. Current multidrug therapy for tuberculosis is exceedingly lengthy, leading to poor drug adherence, and consequently the emergence of drug resistance. Hence, much more rapid treatments are needed. Experimentally identifying the most synergistic drug combinations among available drugs is complicated by the astronomical number of possible drug-dose combinations. This problem is dealt with by the use of an artificial-intelligence-enabled parabolic response surface platform in conjunction with an in vitro Mycobacterium tuberculosis–infected macrophage cell culture assay amenable to high-throughput screening. This strategy allows rapid identification of the most effective drug-dose combinations by testing only a small fraction of the total drug-dose efficacy response surface. The same platform is then used to optimize the in vivo doses of each drug in the most potent regimens. Thus, regimens are identified that are dramatically more effective than the Standard Regimen in treating TB in a mouse model—a model broadly predictive of drug efficacy in humans. The most effective regimens reported herein shorten the duration of treatment required to achieve relapse-free cure by 80% and are suitable for treating both drug-sensitive and most drug-resistant cases of tuberculosis.

Artificial intelligence in nanomedicine

The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.

Optimization of Differentiation of Nonhuman Primate Pluripotent Cells Using a Combinatorial Approach

The directed differentiation of pluripotent stem cells to a desired lineage often involves complex and lengthy protocols. In order to study the requirements for differentiation in a systematic way, we present here methodology for an iterative approach using combinations of small molecules and biological factors. The factors are used in a cyclical process in which the best combination of factors and concentrations is selected in one round of testing, followed by a modification of the combination and subsequent rounds. While this may produce the desired differentiation in the cell population under study, it is also possible that other strategies may be needed to optimize the differentiation process. These strategies are described in this chapter.

Anthelmintic drug actions in resistant and susceptible C. elegans revealed by electrophysiological recordings in a multichannel microfluidic device

Many anthelmintic drugs used to treat parasitic nematode infections target proteins that regulate electrical activity of neurons and muscles: ion channels (ICs) and neurotransmitter receptors (NTRs). Perturbation of IC/NTR function disrupts worm behavior and can lead to paralysis, starvation, immune attack and expulsion. Limitations of current anthelmintics include a limited spectrum of activity across species and the threat of drug resistance, highlighting the need for new drugs for human and veterinary medicine. Although ICs/NTRs are valuable anthelmintic targets, electrophysiological recordings are not commonly included in drug development pipelines. We designed a medium-throughput platform for recording electropharyngeograms (EPGs)-the electrical signals emitted by muscles and neurons of the pharynx during pharyngeal pumping (feeding)-in Caenorhabditis elegans and parasitic nematodes. The current study in C. elegans expands previous work in several ways. Detecting anthelmintic bioactivity in drugs, compounds or natural products requires robust, sustained pharyngeal pumping under baseline conditions. We generated concentration-response curves for stimulating pumping by perfusing 8-channel microfluidic devices (chips) with the neuromodulator serotonin, or with E. coli bacteria (C. elegans' food in the laboratory). Worm orientation in the chip (head-first vs. tail-first) affected the response to E. coli but not to serotonin. Using a panel of anthelmintics-ivermectin, levamisole and piperazine-targeting different ICs/NTRs, we determined the effects of concentration and treatment duration on EPG activity, and successfully distinguished control (N2) and drug-resistant worms (avr-14; avr-15; glc-1, unc-38 and unc-49). EPG recordings detected anthelmintic activity of drugs that target ICs/NTRs located in the pharynx as well as at extra-pharyngeal sites. A bus-8 mutant with enhanced permeability was more sensitive than controls to drug treatment. These results provide a useful framework for investigators who would like to more easily incorporate electrophysiology as a routine component of their anthelmintic research workflow.

Microfluidic Devices for Drug Assays

In this review, we give an overview of the current state of microfluidic-based high-throughput drug assays. In this highly interdisciplinary research field, various approaches have been applied to high-throughput drug screening, including microtiter plate, droplets microfluidics as well as continuous flow, diffusion and concentration gradients-based microfluidic drug assays. Therefore, we reviewed over 100 recent publications in the field and sorted them according to their microfluidic approach. As a result, we are showcasing, comparing and discussing broadly applied approaches as well as singular promising ones that might contribute to shaping the future of this field.

Application of Feedback System Control Optimization Technique in Combined Use of Dual Antiplatelet Therapy and Herbal Medicines

Aim: Combined use of herbal medicines in patients underwent dual antiplatelet therapy (DAPT) might cause bleeding or thrombosis because herbal medicines with anti-platelet activities may exhibit interactions with DAPT. In this study, we tried to use a feedback system control (FSC) optimization technique to optimize dose strategy and clarify possible interactions in combined use of DAPT and herbal medicines.

Methods: Herbal medicines with reported anti-platelet activities were selected by searching related references in Pubmed. Experimental anti-platelet activities of representative compounds originated from these herbal medicines were investigated using in vitro assay, namely ADP-induced aggregation of rat platelet-rich-plasma. FSC scheme hybridized artificial intelligence calculation and bench experiments to iteratively optimize 4-drug combination and 2-drug combination from these drug candidates.

Results: Totally 68 herbal medicines were reported to have anti-platelet activities. In the present study, 7 representative compounds from these herbal medicines were selected to study combinatorial drug optimization together with DAPT, i.e., aspirin and ticagrelor. FSC technique first down-selected 9 drug candidates to the most significant 5 drugs. Then, FSC further secured 4 drugs in the optimal combination, including aspirin, ticagrelor, ferulic acid from DangGui, and forskolin from MaoHouQiaoRuiHua. Finally, FSC quantitatively estimated the possible interactions between aspirin:ticagrelor, aspirin:ferulic acid, ticagrelor:forskolin, and ferulic acid:forskolin. The estimation was further verified by experimentally determined Combination Index (CI) values.

Conclusion: Results of the present study suggested that FSC optimization technique could be used in optimization of anti-platelet drug combinations and might be helpful in designing personal anti-platelet therapy strategy. Furthermore, FSC analysis could also identify interactions between different drugs which might provide useful information for research of signal cascades in platelet.

High-Throughput Isolation of Circulating Tumor Cells Using Cascaded Inertial Focusing Microfluidic Channel

Circulating tumor cells (CTCs) are rare cells that detach from a primary or metastasis tumor and flow into the bloodstream. Intact and viable tumor cells are needed for genetic characterization of CTCs, new drug development, and other research. Although separation of CTCs using spiral channel with two outlets has been reported, few literature demonstrated simultaneous isolation of different types of CTCs from human blood using cascaded inertial focusing microfluidic channel. Herein, we introduce a cascaded microfluidic device consisting of two spiral channels and one zigzag channel designed with different fluid fields, including lift force, Dean drag force, and centrifugal force. Both red blood cells (RBCs)-lysed human blood spiked with CTCs and 1:50 diluted human whole blood spiked with CTCs were tested on the presented chip. This chip successfully separated RBCs, white blood cells (WBCs), and two different types of tumor cells (human lung cancer cells (A549) and human breast cancer cells (MCF-7)) simultaneously based on their physical properties. A total of 80.75% of A549 and 73.75% of MCF-7 were faithfully separated from human whole blood. Furthermore, CTCs gathered from outlets could propagate and remained intact. The cell viability of A549 and MCF-7 were 95% and 98%, respectively. The entire separating process for CTCs from blood cells could be finished within 20 min. The cascaded microfluidic device introduced in this study serves as a novel platform for simultaneous isolation of multiple types of CTCs from patient blood.