Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology

Theranostic Nanoparticles for Tracking and Monitoring Disease State

The development of novel nanoparticles consisting of both diagnostic and therapeutic components has increased over the past decade. These "theranostic" nanoparticles have been tailored toward one or more types of imaging modalities and have been developed for optical imaging, magnetic resonance imaging, ultrasound, computed tomography, and nuclear imaging comprising both single-photon computed tomography and positron emission tomography. In this review, we focus on state-of-the-art theranostic nanoparticles that are capable of both delivering therapy and self-reporting/tracking disease through imaging. We discuss challenges and the opportunity to rapidly adjust treatment for individualized medicine.

Searching Synergistic Dose Combinations for Anticancer Drugs

Recent development has enabled synergistic drugs in treating a wide range of cancers. Being highly context-dependent, however, identification of successful ones often requires screening of combinational dose on different testing platforms in order to gain the best anticancer effects. To facilitate the development of effective computational models, we reviewed the latest strategy in searching optimal dose combination from three perspectives: (1) mainly experimental-based approach; (2) Computational-guided experimental approach; and (3) mainly computational-based approach. In addition to the introduction of each strategy, critical discussion of their advantages and disadvantages were also included, with a strong focus on the current applications and future improvements.

Predicting the impact of structural diversity on the performance of nanodiamond drug carriers

Diamond nanoparticles (nanodiamonds) are unique among carbon nanomaterials, and are quickly establishing a niché in the biomedical application domain. Nanodiamonds are non-toxic, amenable to economically viable mass production, and can be interfaced with a variety of functional moieties. However, developmental challenges arise due to the chemical complexity and structural diversity inherent in nanodiamond samples. Nanodiamonds present a narrow, but significant, distribution of sizes, a dizzying array of possible shapes, and a complicated surface containing aliphatic and aromatic carbon. In the past these facts have been cast as hindrances, stalling development until perfectly monodispersed samples could be achieved. Current research has moved in a different direction, exploring ways that the polydispersivity of nanodiamond samples can be used as a new degree of engineering freedom, and understanding the impact our limited synthetic control really has upon structure/property relationships. In this review a series of computational and statistical studies will be summarised and reviewed, to characterise the relationship between chemical complexity, structural diversity and the reactive performance of nanodiamond drug carriers.

Clinical validation of a nanodiamond-embedded thermoplastic biomaterial

Detonation nanodiamonds (NDs) are promising drug delivery and imaging agents due to their uniquely faceted surfaces with diverse chemical groups, electrostatic properties, and biocompatibility. Based on the potential to harness ND properties to clinically address a broad range of disease indications, this work reports the in-human administration of NDs through the development of ND-embedded gutta percha (NDGP), a thermoplastic biomaterial that addresses reinfection and bone loss following root canal therapy (RCT). RCT served as the first clinical indication for NDs since the procedure sites involved nearby circulation, localized administration, and image-guided treatment progress monitoring, which are analogous to many clinical indications. This randomized, single-blind interventional treatment study evaluated NDGP equivalence with unmodified GP. This progress report assessed one control-arm and three treatment-arm patients. At 3-mo and 6-mo follow-up appointments, no adverse events were observed, and lesion healing was confirmed in the NDGP-treated patients. Therefore, this study is a foundation for the continued clinical translation of NDs and other nanomaterials for a broad spectrum of applications.

Feedback System Control Optimized Electrospinning for Fabrication of an Excellent Superhydrophobic Surface

Superhydrophobic surface, as a promising micro/nano material, has tremendous applications in biological and artificial investigations. The electrohydrodynamics (EHD) technique is a versatile and effective method for fabricating micro- to nanoscale fibers and particles from a variety of materials. A combination of critical parameters, such as mass fraction, ratio of N, N-Dimethylformamide (DMF) to Tetrahydrofuran (THF), inner diameter of needle, feed rate, receiving distance, applied voltage as well as temperature, during electrospinning process, to determine the morphology of the electrospun membranes, which in turn determines the superhydrophobic property of the membrane. In this study, we applied a recently developed feedback system control (FSC) scheme for rapid identification of the optimal combination of these controllable parameters to fabricate superhydrophobic surface by one-step electrospinning method without any further modification. Within five rounds of experiments by testing totally forty-six data points, FSC scheme successfully identified an optimal parameter combination that generated electrospun membranes with a static water contact angle of 160 degrees or larger. Scanning electron microscope (SEM) imaging indicates that the FSC optimized surface attains unique morphology. The optimized setup introduced here therefore serves as a one-step, straightforward, and economic approach to fabricate superhydrophobic surface with electrospinning approach.

Biomedical applications of nanodiamond (Review)

The interest in nanodiamond applications in biology and medicine is on the rise over recent years. This is due to the unique combination of properties that nanodiamond provides. Small size (∼5 nm), low cost, scalable production, negligible toxicity, chemical inertness of diamond core and rich chemistry of nanodiamond surface, as well as bright and robust fluorescence resistant to photobleaching are the distinct parameters that render nanodiamond superior to any other nanomaterial when it comes to biomedical applications. The most exciting recent results have been related to the use of nanodiamonds for drug delivery and diagnostics-two components of a quickly growing area of biomedical research dubbed theranostics. However, nanodiamond offers much more in addition: it can be used to produce biodegradable bone surgery devices, tissue engineering scaffolds, kill drug resistant microbes, help us to fight viruses, and deliver genetic material into cell nucleus. All these exciting opportunities require an in-depth understanding of nanodiamond. This review covers the recent progress as well as general trends in biomedical applications of nanodiamond, and underlines the importance of purification, characterization, and rational modification of this nanomaterial when designing nanodiamond based theranostic platforms.

Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization

Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

Hollow Mesoporous Silica Nanoparticles with Tunable Structures for Controlled Drug Delivery

A size-controllable and facile synthetic strategy has been developed to fabricate a series of hollow mesoporous silica nanoparticles (HMSNs) with tunable hollow cores or shell thicknesses by employing gold nanoparticles (Au NPs) and cetyltrimethylammonium bromide (CTAB) as dual templates. Various sizes of Au NPs and different amounts of tetraethyl orthosilicate contributed to structure-tailored mesoporous silica-coated Au NPs. After calcination, CTAB molecules were completely removed, and Au NPs could still support the silica shell due to the high melting point. HMSNs were ultimately obtained by etching Au NPs. Applications of HMSNs as nanocarriers for delivering drugs were investigated. Significantly, it was flexible and convenient to control drug-loading/releasing behavior of HMSNs just by tuning the hollow cores or shell thicknesses. Intracellular experiments have proven that HMSNs are suitable for delivering drugs. We anticipate that this study could provide an important avenue for the synthesis of HMSNs and further contribute to advancing practical applications of HMSNs in drug delivery systems.

Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis.

Current antibiotic therapies for tuberculosis are lengthy and onerous. Here, the authors use an output-driven approach to optimize drug doses for two experimental drug regimens in a mouse model of tuberculosis, leading to improved regimens that reduce treatment time by 75%.

Diamond nanostructures for drug delivery, bioimaging, and biosensing

This review summarizes the superior properties of diamond nanoparticles and vertically aligned diamond nanoneedles and their applications in biosensing, bioimaging and drug delivery.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Optimizing Combination Therapy for Acute Lymphoblastic Leukemia Using a Phenotypic Personalized Medicine Digital Health Platform: Retrospective Optimization Individualizes Patient Regimens to Maximize Efficacy and Safety

Acute lymphoblastic leukemia (ALL) is a blood cancer that is characterized by the overproduction of lymphoblasts in the bone marrow. Treatment for pediatric ALL typically uses combination chemotherapy in phases, including a prolonged maintenance phase with oral methotrexate and 6-mercaptopurine, which often requires dose adjustment to balance side effects and efficacy. However, a major challenge confronting combination therapy for virtually every disease indication is the inability to pinpoint drug doses that are optimized for each patient, and the ability to adaptively and continuously optimize these doses to address comorbidities and other patient-specific physiological changes. To address this challenge, we developed a powerful digital health technology platform based on phenotypic personalized medicine (PPM). PPM identifies patient-specific maps that parabolically correlate drug inputs with phenotypic outputs. In a disease mechanism-independent fashion, we individualized drug ratios/dosages for two pediatric patients with standard-risk ALL in this study via PPM-mediated retrospective optimization. PPM optimization demonstrated that dynamically adjusted dosing of combination chemotherapy could enhance treatment outcomes while also substantially reducing the amount of chemotherapy administered. This may lead to more effective maintenance therapy, with the potential for shortening duration and reducing the risk of serious side effects.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Diamonds, Digital Health, and Drug Development: Optimizing Combinatorial Nanomedicine

The field of nanomedicine has already seen substantial progress in the clinic, with multiple formulations being evaluated through clinical studies. From poly(lactic-co-glycolic acid) and cyclodextrin-based drug-delivery platforms to metallic nanoparticles for photothermal treatment and imaging, nanotechnology has enabled versatile strategies to treat and to diagnose a wide range of disorders. However, as the field as a whole pushes forward, barriers that have always challenged conventional drug development have already started to impact nanomedicine translation. These include exorbitant costs, substantial time to development, and the uncertainty of achieving major improvements in efficacy or safety. Of note, there has been, until recent advances, a virtual inability to identify optimal drug doses either as monotherapies or components of combination therapy. In this Nano Focus, we assess how the impact of nanotechnology in the clinic can be optimized through systematically designed combinatorial nanotherapy. In addition, we provide a clinical perspective on how a recently unveiled technology platform can substantially alter the landscape of combinatorial nanomedicine, drug development, as well as conventional drug development.

Biocompatibility Assessment of Detonation Nanodiamond in Non-Human Primates and Rats Using Histological, Hematologic, and Urine Analysis

Detonation nanodiamonds (DNDs) have been widely explored for biomedical applications ranging from cancer therapy to magnetic resonance imaging due to several promising properties. These include faceted surfaces that mediate potent drug binding and water coordination that have resulted in marked enhancements to the efficacy and safety of drug delivery and imaging. In addition, scalable processing of DNDs yields uniform particles. Furthermore, a broad spectrum of biocompatibility studies has shown that DNDs appear to be well-tolerated. Prior to the clinical translation of DNDs for indications that are addressed via intravenous administration, comprehensive assessment of DND safety in both small and large animal preclinical models is needed. This article reports the results of a DND biocompatibility study in both non-human primates and rats. The rat study was performed as a multiple dose subacute investigation in two cohorts that lasted for 2 weeks and included histological, serum, and urine analysis. The non-human primate study was performed as a dual gender, multiple dose, and long-term investigation in both standard/clinically relevant and elevated dosing cohorts that lasted for 6 months and included comprehensive serum, urine, histological, and body weight analysis. The results from these studies indicate that NDs are well-tolerated at clinically relevant doses. Examination of dose-dependent changes in biomarker levels provides important guidance for the downstream in-human validation of DNDs for clinical drug delivery and imaging.

Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform

Postoperative liver transplant immunosuppression was personalized using a phenotypic, disease mechanism–independent and indication-agnostic approach.

Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug-dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug-dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three- and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three- and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug- and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug-dose combinations for treatment of TB.

Challenges in modelling nanoparticles for drug delivery

Although there have been significant advances in the fields of theoretical condensed matter and computational physics, when confronted with the complexity and diversity of nanoparticles available in conventional laboratories a number of modeling challenges remain. These challenges are generally shared among application domains, but the impacts of the limitations and approximations we make to overcome them (or circumvent them) can be more significant one area than another. In the case of nanoparticles for drug delivery applications some immediate challenges include the incompatibility of length-scales, our ability to model weak interactions and solvation, the complexity of the thermochemical environment surrounding the nanoparticles, and the role of polydispersivity in determining properties and performance. Some of these challenges can be met with existing technologies, others with emerging technologies including the data-driven sciences; some others require new methods to be developed. In this article we will briefly review some simple methods and techniques that can be applied to these (and other) challenges, and demonstrate some results using nanodiamond-based drug delivery platforms as an exemplar.

Optimization of drug combinations using Feedback System Control

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

A UV-sensitive hydrogel based combinatory drug delivery chip (UV gel-Drug Chip) for cancer cocktail drug screening

Photosensitive PEGDA hydrogel based platform for high dynamic range testing of combinatorial cocktail drug screening by using FSC method for colon cancer.

A streamlined search technology for identification of synergistic drug combinations

A major key to improvement of cancer therapy is the combination of drugs. Mixing drugs that already exist on the market may offer an attractive alternative. Here we report on a new model-based streamlined feedback system control (s-FSC) method, based on a design of experiment approach, for rapidly finding optimal drug mixtures with minimal experimental effort. We tested combinations in an in vitro assay for the viability of a renal cell adenocarcinoma (RCC) cell line, 786-O. An iterative cycle of in vitro testing and s-FSC analysis was repeated a few times until an optimal low dose combination was reached. Starting with ten drugs that target parallel pathways known to play a role in the development and progression of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitinib, erlotinib, dasatinib and AZD4547) at low doses, inhibiting 90% of cell viability. The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhibition, while still maintaining the synergistic interaction. These optimized drug combinations were significantly more potent than monotherapies of all individual drugs (p < 0.001, CI < 0.3).

Quantitative dose-response curves from subcellular lipid multilayer microarrays

The dose-dependent bioactivity of small molecules on cells is a crucial factor in drug discovery and personalized medicine. Although small-molecule microarrays are a promising platform for miniaturized screening, it has been a challenge to use them to obtain quantitative dose-response curves in vitro, especially for lipophilic compounds. Here we establish a small-molecule microarray assay capable of controlling the dosage of small lipophilic molecules delivered to cells by varying the sub-cellular volumes of surface supported lipid micro- and nanostructure arrays fabricated with nanointaglio. Features with sub-cellular lateral dimensions were found necessary to obtain normal cell adhesion with HeLa cells. The volumes of the lipophilic drug-containing nanostructures were determined using a fluorescence microscope calibrated by atomic-force microscopy. We used the surface supported lipid volume information to obtain EC-50 values for the response of HeLa cells to three FDA-approved lipophilic anticancer drugs, docetaxel, imiquimod and triethylenemelamine, which were found to be significantly different from neat lipid controls. No significant toxicity was observed on the control cells surrounding the drug/lipid patterns, indicating lack of interference or leakage from the arrays. Comparison of the microarray data to dose-response curves for the same drugs delivered liposomally from solution revealed quantitative differences in the efficacy values, which we explain in terms of cell-adhesion playing a more important role in the surface-based assay. The assay should be scalable to a density of at least 10,000 dose response curves on the area of a standard microtiter plate.

Nanodiamonds: The intersection of nanotechnology, drug development, and personalized medicine

The implementation of nanomedicine in cellular, preclinical, and clinical studies has led to exciting advances ranging from fundamental to translational, particularly in the field of cancer. Many of the current barriers in cancer treatment are being successfully addressed using nanotechnology-modified compounds. These barriers include drug resistance leading to suboptimal intratumoral retention, poor circulation times resulting in decreased efficacy, and off-target toxicity, among others. The first clinical nanomedicine advances to overcome these issues were based on monotherapy, where small-molecule and nucleic acid delivery demonstrated substantial improvements over unmodified drug administration. Recent preclinical studies have shown that combination nanotherapies, composed of either multiple classes of nanomaterials or a single nanoplatform functionalized with several therapeutic agents, can image and treat tumors with improved efficacy over single-compound delivery. Among the many promising nanomaterials that are being developed, nanodiamonds have received increasing attention because of the unique chemical-mechanical properties on their faceted surfaces. More recently, nanodiamond-based drug delivery has been included in the rational and systematic design of optimal therapeutic combinations using an implicitly de-risked drug development platform technology, termed Phenotypic Personalized Medicine-Drug Development (PPM-DD). The application of PPM-DD to rapidly identify globally optimized drug combinations successfully addressed a pervasive challenge confronting all aspects of drug development, both nano and non-nano. This review will examine various nanomaterials and the use of PPM-DD to optimize the efficacy and safety of current and future cancer treatment. How this platform can accelerate combinatorial nanomedicine and the broader pharmaceutical industry toward unprecedented clinical impact will also be discussed.

Accelerating the Translation of Nanomaterials in Biomedicine

Due to their size and tailorable physicochemical properties, nanomaterials are an emerging class of structures utilized in biomedical applications. There are now many prominent examples of nanomaterials being used to improve human health, in areas ranging from imaging and diagnostics to therapeutics and regenerative medicine. An overview of these examples reveals several common areas of synergy and future challenges. This Nano Focus discusses the current status and future potential of promising nanomaterials and their translation from the laboratory to the clinic, by highlighting a handful of successful examples.