Clinical biomarkers in drug discovery and development

Small Molecule with Big Impact: Metarrestin Targets the Perinucleolar Compartment in Cancer Metastasis

Metarrestin (ML246) is a first-in-class pyrrole-pyrimidine-derived small molecule that selectively targets the perinucleolar compartment (PNC). PNC is a distinct subnuclear structure predominantly found in solid tumor cells. The occurrence of PNC demonstrates a positive correlation with malignancy, serving as an indicator of tumor aggressiveness, progression, and metastasis. Various promising preclinical results have led to the clinical translation of metarrestin into a first-in-human trial. This review aims to summarize (i) the current understanding of the structure and function of PNC and its role in cancer progression and metastasis, (ii) key findings from studies examining the effect of metarrestin on various cancers across the translational spectrum, including in vitro, in vivo, and human clinical trial studies, and (iii) the pharmaceutical relevance of metarrestin as a promising anticancer candidate. Furthermore, our molecular docking and MD simulation studies show that metarrestin binds to eEF1A1 and eEF1A2 with a strong and stable affinity and inhibits eEF1A2 more efficiently compared to eEF1A1. The promising results from preclinical studies suggest that metarrestin has the potential to revolutionize the treatment of cancer, heralding a paradigm shift in its therapeutic management.

New drug discovery and development from natural products: Advances and strategies

Natural products (NPs) have a long history as sources for drug discovery, more than half of approved drugs are related to NPs, which also exhibit multifaceted advantages in the clinical treatment of complex diseases. However, bioactivity screening of NPs, target identification, and design optimization require continuously improved strategies, the complexity of drug mechanism of action and the limitations of technological strategies pose numerous challenges to the development of new drugs. This review begins with an overview of bioactivity- and target-based drug development patterns for NPs, advances in NP screening and derivatization, and the advantages and problems of major targets such as genes and proteins. Then, target-based drugs as well as identification and validation methods are further discussed to elucidate their mechanism of action. Subsequently, the current status and development trend of the application of traditional and emerging technologies in drug discovery and development of NPs are systematically described. Finally, the collaborative strategy of multi-technology integration and multi-disciplinary intersection is emphasized for the challenges faced in the identification, optimization, activity evaluation, and clinical application of NPs. It is hoped to provide a systematic overview and inspiration for exploring new drugs from natural resources in the future.

Identification of potential differences in salivary proteomic profiles between estrus and diestrus stage of estrous cycle in dairy cows

In the present study, a comparative global high-throughput proteomic analysis strategy was used to identify proteomic differences between estrus and diestrus stage of estrous cycle in dairy cows. Saliva was collected from cows during estrus and diestrus, and subjected to LC-MS/MS-based proteomic analysis. A total of 2842 proteins were detected in the saliva of cows, out of which, 2437 and 1428 non-redundant proteins were identified in estrous and diestrous saliva, respectively. Further, it was found that 1414 and 405 salivary proteins were specific to estrus and diestrus, respectively while 1023 proteins were common to both groups. Among the significantly dysregulated proteins, the expression of 56 proteins was down-regulated (abundance ratio <0.5) while 40 proteins were up-regulated (abundance ratio > 2) in estrous compared to diestrous saliva. The proteins, such as HSD17B12, INHBA, HSP70, ENO1, SRD5A1, MOS, AMH, ECE2, PDGFA, OPRK1, SYN1, CCNC, PLIN5, CETN1, AKR1C4, NMNAT1, CYP2E1, and CYP19A1 were detected only in the saliva samples derived from estrous cows. Considerable number of proteins detected in the saliva of estrous cows were found to be involved in metabolic pathway, PI3K-Akt signaling pathway, toll-like receptor signaling pathway, steroid biosynthesis pathway, insulin signaling pathway, calcium signaling pathway, estrogen signaling pathway, oxytocin signaling pathway, TGF-β signaling pathway and oocyte meiosis. On the other hand, proteins detected in saliva of diestrous cows were involved mainly in metabolic pathway. Collectively, these data provide preliminary evidence of a potential difference in salivary proteins at different stages of estrous cycle in dairy cows.

Biomarkers in asthma, potential for therapeutic intervention

Asthma is a heterogeneous disease characterized by multiple phenotypes with varying risk factors and therapeutic responses. This Commentary describes research on biomarkers for T2-"high" and T2-"low" inflammation, a hallmark of the disease. Patients with asthma who exhibit an increase in airway T2 inflammation are classified as having T2-high asthma. In this endotype, Type 2 cytokines interleukins (IL)-4, IL-5, and IL-13, plus other inflammatory mediators, lead to increased eosinophilic inflammation and elevated fractional exhaled nitric oxide (FeNO). In contrast, T2-low asthma has no clear definition. Biomarkers are considered valuable tools as they can help identify various phenotypes and endotypes, as well as treatment response to standard treatment or potential therapeutic targets, particularly for biologics. As our knowledge of phenotypes and endotypes expands, biologics are increasingly integrated into treatment strategies for severe asthma. These treatments block specific inflammatory pathways or single mediators. While single or composite biomarkers may help to identify subsets of patients who might benefit from these treatments, only a few inflammatory biomarkers have been validated for clinical application. One example is sputum eosinophilia, a particularly useful biomarker, as it may suggest corticosteroid responsiveness or reflect non-compliance to inhaled corticosteroids. As knowledge develops, a meaningful goal would be to provide individualized care to patients with asthma.

Selection and direct biomarkers of reproductive capacity of breeding boars

Efficient management of pig reproduction is paramount for the sustainability and productivity of the global pork industry. Modern artificial insemination (AI) breeding programs have greatly benefited from the integration of advanced selection methods and biomarkers to enhance the reproductive performance of boars. While traditional selection methods have relied soley on boar phenotype, such as growth rate and conformation, modern pig breeding has shifted more and more toward molecular and genetic tools, which are still complemented by phenotypic traits. These methods encompass genomics, transcriptomics, and proteomics. Biomarkers serve as critical indicators of boar reproductive capacity. They can help to identify individuals with superior fertility and aid in the early identification of potential fertility issues, allowing for proactive management strategies. This review summarizes current knowledge of various biomarkers associated with semen quality, sperm function, and overall reproductive fitness in boars. Furthermore, we explore advanced technologies and their potential applications in uncovering novel selection methods and biomarkers for predicting boar fertility. A comprehensive understanding of selection criteria and biomarkers governing boar reproductive capacity is essential for developing effective breeding programs to enhance swine reproductive performance.

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.

Wearable and implantable biosensors: mechanisms and applications in closed-loop therapeutic systems

This review article examines the current state of wearable and implantable biosensors, offering an overview of their biosensing mechanisms and applications. We also delve into integrating these biosensors with therapeutic systems, discussing their operational principles and incorporation into closed-loop devices. Biosensing strategies are broadly categorized into chemical sensing for biomarker detection, physical sensing for monitoring physiological conditions such as pressure and temperature, and electrophysiological sensing for capturing bioelectrical activities. The discussion extends to recent developments in drug delivery and electrical stimulation devices to highlight their significant role in closed-loop therapy. By integrating with therapeutic devices, biosensors enable the modulation of treatment regimens based on real-time physiological data. This capability enhances the patient-specificity of medical interventions, an essential aspect of personalized healthcare. Recent innovations in integrating biosensors and therapeutic devices have led to the introduction of closed-loop wearable and implantable systems capable of achieving previously unattainable therapeutic outcomes. These technologies represent a significant leap towards dynamic, adaptive therapies that respond in real-time to patients' physiological states, offering a level of accuracy and effectiveness that is particularly beneficial for managing chronic conditions. This review also addresses the challenges associated with biosensor technologies. We also explore the prospects of these technologies to address their potential to transform disease management with more targeted and personalized treatment solutions.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

In vitro demonstration of antedrug mechanism of a pharmacokinetic booster to improve CYP3A4 substrates by CYP3A4-mediated metabolism inhibition

We previously reported novel benzyl-ether derivatives with an imidazole ring and a hydroxyl group (A-01) or carboxyl group (B-01) and esters (2 esters of A-01, and 7 esters of B-01) as pharmacokinetics (PK) boosters. This study demonstrates how these ester compounds embody the concept of a safe pharmacokinetic booster, with potent and transient inhibition of CYP3A4-mediated drug metabolism. As a model CYP3A4 substrate and CYP3A4 enzyme, midazolam (MDZ) and rat liver microsomes were used. A-01 inhibited MDZ metabolism significantly, while B-01 induced only slight inhibition. Although rat liver microsomes hydrolyzed the ester compounds over time, several ester compounds strongly inhibited MDZ metabolism. Due to the significant activity of A-01, A-01 esters affected MDZ metabolism, irrespective of hydrolysis state. Time-dependent inhibition evaluation indicated that the B-01 ester inhibition is not mechanism-based, as hydrolysis eliminated MDZ metabolism inhibition. We report that the B-01 esters significantly inhibit CYP3A4-mediated drug metabolism, and upon hydrolysis this property is eliminated. In conclusion, B-01 ester compounds may be safe PK boosters with antedrug characteristics.

Accelerating protein aggregation and amyloid fibrillation for rapid inhibitor screening

The accumulation and deposition of amyloid fibrils, also known as amyloidosis, in tissues and organs of patients has been found to be linked to numerous devastating neurodegenerative diseases. The aggregation of proteins to form amyloid fibrils, however, is a slow pathogenic process, and is a major issue for the evaluation of the effectiveness of inhibitors in new drug discovery and screening. Here, we used microdroplet reaction technology to accelerate the amyloid fibrillation process, monitored the process to shed light on the fundamental mechanism of amyloid self-assembly, and demonstrated the value of the technology in the rapid screening of potential inhibitor drugs. Proteins in microdroplets accelerated to form fibrils in milliseconds, enabling an entire cycle of inhibitor screening for Aβ40 within 3 minutes. The technology would be of broad interest to drug discovery and therapeutic design to develop treatments for diseases associated with protein aggregation and fibrillation.

Developing medicines for the pre-symptomatic stage of degenerative neurological conditions: Challenges and opportunities

Neurodegenerative disorders have a devastating disease course and an increasing prevalence due to prolonged life expectancy. In the last decades, it has become increasingly clear that these diseases often start much earlier, before the onset of symptoms, creating a potential window for pre-symptomatic treatment, a strategy that is desirable from both the biologic and the ethical point of view. However, studying treatments for a pre-symptomatic population presents objective difficulties. This article intends to give a perspective about opportunities and challenges of pre-symptomatic prevention of neurodegenerative diseases. Besides the requirement for biomarkers that would facilitate both the selection of study population and demonstrating a treatment effect, further considerations about balancing benefits, risks and uncertainties pertaining a pre-symptomatic population will be examined.

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNN

Spatially resolved transcriptomics or proteomics data have the potential to contribute fundamental insights into the mechanisms underlying physiologic and pathological processes. However, analysis of these data capable of relating spatial information, multiplexed markers, and their observed phenotypes remains technically challenging. To analyze these relationships, we developed SORBET, a deep learning framework that leverages recent advances in graph neural networks (GNN). We apply SORBET to predict tissue phenotypes, such as response to immunotherapy, across different disease processes and different technologies including both spatial proteomics and transcriptomics methods. Our results show that SORBET accurately learns biologically meaningful relationships across distinct tissue structures and data acquisition methods. Furthermore, we demonstrate that SORBET facilitates understanding of the spatially-resolved biological mechanisms underlying the inferred phenotypes. In sum, our method facilitates mapping between the rich spatial and marker information acquired from spatial 'omics technologies to emergent biological phenotypes. Moreover, we provide novel techniques for identifying the biological processes that comprise the predicted phenotypes.

A one-pot transcriptional assay method that detects the tumor biomarker FEN1 based on its flap cleavage activity

BACKGROUND

Detection of tumor biomarkers in body fluids is a significant advancement in cancer treatment because it allows diagnosis without invasive tissue biopsies. Nucleases have long been regarded as a potential class of biomarkers that can indicate the occurrence and progression of cancers. Among these, flap endonuclease 1 (FEN1) plays an important role in DNA replication and repair, and also overexpressed in abnormally proliferating cells such as cancer cells. FEN1 is thus considered to be a potential biomarker as well as a target for cancer therapy.

RESULTS

We developed a novel method for detecting FEN1 based on its specific endonuclease activity which incises bifurcated nucleic acids (flaps), in combination with in vitro transcription. Developed method uses a simple DNA structure (substrate DNA) carrying a short 5'-flap sequence, and a single-stranded sensor DNA encoding the Broccoli light-up aptamer. When the assay mixture was supplied with a FEN1-containing sample, the flap sequence encoding the sense sequence of T7 promoter was cleaved and released from the substrate DNA. Because the sensor DNA was designed to carry the Broccoli RNA aptamer under the antisense sequence of T7 promoter, hybridization of the excised flap onto the sensor DNA initiated the transcription of the Broccoli RNA aptamer, enabling determination of the FEN1 titer based on the fluorescence of transcribed Broccoli aptamer. By using a combination of FEN1-mediated generation of a short oligonucleotide and subsequent oligonucleotide-dependent in vitro transcription, this method could detect FEN1 in biological samples within 1 h.

SIGNIFICANCE AND NOVELTY

Developed method enables the detection of FEN1 by a simple one-pot reaction. It can detect sub-nanomolar concentrations of FEN1 within an hour, and has the potential to be used for cancer diagnosis, prognosis, and drug screening. It also enables easy identification of compounds that inhibit FEN1 activity and is thus a versatile platform for screening anti-cancer drugs. We anticipate that the basic principles of this assay can be applied to detect other biomolecules, such as nucleic acids.

Serum extracellular vesicles with NSD1 and FBXO7 mRNA as novel biomarkers for gastric cancer

BACKGROUND

Messenger RNAs (mRNAs) in serum extracellular vesicles (EVs) are effective non-invasive biomarkers for various types of cancer, however, their role as biomarkers for gastric cancer is yet to be investigated. Therefore, the current study was designed to explore their potential as novel biomarkers for gastric cancer.

METHODS

The mRNAs in serum EVs from four patients with gastric cancer and four healthy controls were investigated. mRNAs in serum EVs were extracted for high-throughput RNA sequencing (RNA-seq). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to predict cancer-related genes. Candidate mRNAs were validated using reverse transcription-quantitative polymerase chain reaction. The diagnostic and prognostic values of mRNAs for gastric cancer were evaluated by receiver operating characteristic (ROC) curves and Kaplan-Meier analysis, respectively.

RESULTS

RNA-seq revealed 13,229 upregulated and 7,079 downregulated mRNAs in serum EVs. GO and KEGG analyses showed that certain mRNAs were associated with tumorigenesis and progression. From these, 10 were selected according to our criteria (|Fold Change| > 10, P < 0.05). NSD1 was upregulated and FBXO7 was downregulated in patients with gastric cancer compared with the healthy controls. The area under the ROC curves of these two mRNAs combined was 0.84, with a sensitivity of 78 % and a specificity of 92 %. NSD1 and FBXO7 were also associated with tumor size, distal metastasis, and TNM stage. Furthermore, NSD1 expression was strongly associated with prognosis, as revealed from our follow-up studies and online database analysis. However, FBXO7 was only significantly associated with prognosis in our follow-up data.

CONCLUSIONS

NSD1 and FBXO7 in serum EVs have important roles in gastric cancer and may be useful biomarkers for its diagnosis and prognosis.

Synthesis of S-Carbamidomethyl Cysteine and Its Use for Quantification of Cysteinyl Peptides by Targeted Proteomics

Proteomics has played a central role in the identification of reliable disease biomarkers, which are the basis of precision medicine, a promising approach for tackling recalcitrant diseases such as cancer, that elude conventional treatments. Among proteomic methodologies, targeted proteomics employing stable isotope-labeled (SIL) internal standards is particularly suited for the clinical translation of biomarker information owing to its high throughput and accuracy in the quantitative analysis of patient-derived proteomes. Using SIL internal standards ensures the utmost level of confidence in detection and precision in targeted MS experiments. For successfully establishing assays based on targeted proteomics, it is crucial to secure broad coverage when selecting the SIL standard peptide panel. However, cysteinyl peptides have often been excluded because of cysteine's high chemical reactivity. To address this limitation, a new cysteine building block was developed by incorporating a sulfhydryl group configured with an S-carbamidomethyl group, which is commonly used in proteome sampling. This compound was found to be chemically stable and applicable to a variety of solid-phase peptide synthesis (SPPS) campaigns. Furthermore, a direct comparison of the synthesized SIL peptides and tryptic endogenous peptides demonstrated the potential utility of an SPPS flow based on the new cysteine building block for improving the success of targeted proteomic applications.

Blood-based biomarkers for Alzheimer's disease: Current state and future use in a transformed global healthcare landscape

Timely detection of the pathophysiological changes and cognitive impairment caused by Alzheimer's disease (AD) is increasingly pressing because of the advent of biomarker-guided targeted therapies that may be most effective when provided early in the disease. Currently, diagnosis and management of early AD are largely guided by clinical symptoms. FDA-approved neuroimaging and cerebrospinal fluid biomarkers can aid detection and diagnosis, but the clinical implementation of these testing modalities is limited because of availability, cost, and perceived invasiveness. Blood-based biomarkers (BBBMs) may enable earlier and faster diagnoses as well as aid in risk assessment, early detection, prognosis, and management. Herein, we review data on BBBMs that are closest to clinical implementation, particularly those based on measures of amyloid-β peptides and phosphorylated tau species. We discuss key parameters and considerations for the development and potential deployment of these BBBMs under different contexts of use and highlight challenges at the methodological, clinical, and regulatory levels.

Circulating hsa-miR-5096 predicts 18F-FDG PET/CT positivity and modulates somatostatin receptor 2 expression: a novel miR-based assay for pancreatic neuroendocrine tumors

Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) are rare diseases encompassing pancreatic (PanNETs) and ileal NETs (SINETs), characterized by heterogeneous somatostatin receptors (SSTRs) expression. Treatments for inoperable GEP-NETs are limited, and SSTR-targeted Peptide Receptor Radionuclide Therapy (PRRT) achieves variable responses. Prognostic biomarkers for the management of GEP-NET patients are required. ¹⁸F-FDG uptake is a prognostic indicator of aggressiveness in GEP-NETs. This study aims to identify circulating and measurable prognostic miRNAs associated with ¹⁸F-FDG-PET/CT status, higher risk and lower response to PRRT.

Methods

Whole miRNOme NGS profiling was conducted on plasma samples obtained from well-differentiated advanced, metastatic, inoperable G1, G2 and G3 GEP-NET patients enrolled in the non-randomized LUX (NCT02736500) and LUNET (NCT02489604) clinical trials prior to PRRT (screening set, n= 24). Differential expression analysis was performed between ¹⁸F-FDG positive (n=12) and negative (n=12) patients. Validation was conducted by Real Time quantitative PCR in two distinct well-differentiated GEP-NET validation cohorts, considering the primary site of origin (PanNETs n=38 and SINETs n=30). The Cox regression was applied to assess independent clinical parameters and imaging for progression-free survival (PFS) in PanNETs. In situ RNA hybridization combined with immunohistochemistry was performed to simultaneously detect miR and protein expression in the same tissue specimens. This novel semi-automated miR-protein protocol was applied in PanNET FFPE specimens (n=9). In vitro functional experiments were performed in PanNET models.

Results

While no miRNAs emerged to be deregulated in SINETs, hsa-miR-5096, hsa-let-7i-3p and hsa-miR-4311 were found to correlate with ¹⁸F-FDG-PET/CT in PanNETs (p-value:<0.005). Statistical analysis has shown that, hsa-miR-5096 can predict 6-month PFS (p-value:<0.001) and 12-month Overall Survival upon PRRT treatment (p-value:<0.05), as well as identify ¹⁸F-FDG-PET/CT positive PanNETs with worse prognosis after PRRT (p-value:<0.005). In addition, hsa-miR-5096 inversely correlated with both SSTR2 expression in PanNET tissue and with the ⁶⁸Gallium-DOTATOC captation values (p-value:<0.05), and accordingly it was able to decrease SSTR2 when ectopically expressed in PanNET cells (p-value:<0.01).

Conclusions

hsa-miR-5096 well performs as a biomarker for ¹⁸F-FDG-PET/CT and as independent predictor of PFS. Moreover, exosome-mediated delivery of hsa-miR-5096 may promote SSTR2 heterogeneity and thus resistance to PRRT.

Symptomatology in 4-repeat tauopathies is associated with data-driven topology of [18F]-PI-2620 tau-PET signal

In recent years in vivo visualization of tau deposits has become possible with various PET radiotracers. The tau tracer [18F]PI-2620 proved high affinity both to 3-repeat/4-repeat tau in Alzheimer's disease as well as to 4-repeat tau in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). However, to be clinically relevant, biomarkers should not only correlate with pathological changes but also with disease stage and progression. Therefore, we aimed to investigate the correlation between topology of [18F]PI-2620 uptake and symptomatology in 4-repeat tauopathies. 72 patients with possible or probable 4-repeat tauopathy, i.e. 31 patients with PSP-Richardson's syndrome (PSP-RS), 30 with amyloid-negative CBS and 11 with PSP-non-RS/CBS, underwent [18F]PI-2620-PET. Principal component analysis was performed to identify groups of similar brain regions based on 20-40 min p.i. regional standardized uptake value ratio z-scores. Correlations between component scores and the items of the PSP Rating Scale were explored. Motor signs like gait, arising from chair and postural instability showed a positive correlation with tracer uptake in mesial frontoparietal lobes and the medial superior frontal gyrus and adjacent anterior cingulate cortex. While the signs disorientation and bradyphrenia showed a positive correlation with tracer uptake in the parietooccipital junction, the signs disorientation and arising from chair were negatively correlated with tau-PET signal in the caudate nucleus and thalamus. Total PSP Rating Scale Score showed a trend towards a positive correlation with mesial frontoparietal lobes and a negative correlation with caudate nucleus and thalamus. While in CBS patients, the main finding was a negative correlation of tracer binding in the caudate nucleus and thalamus and a positive correlation of tracer binding in medial frontal cortex with gait and motor signs, in PSP-RS patients various correlations of clinical signs with tracer binding in specific cerebral regions could be detected. Our data reveal [18F]PI-2620 tau-PET topology to correlate with symptomatology in 4-repeat tauopathies. Longitudinal studies will be needed to address whether a deterioration of signs and symptoms over time can be monitored by [18F]PI-2620 in 4-repeat tauopathies and whether [18F]PI-2620 may serve as a marker of disease progression in future therapeutic trials. The detected negative correlation of tracer binding in the caudate nucleus and thalamus with the signs disorientation and arising from chair may be due to an increasing atrophy in these regions leading to partial volume effects and a relative decrease of tracer uptake in the disease course. As cerebral regions correlating with symptomatology differ depending on the clinical phenotype, a precise knowledge of clinical signs and symptoms is necessary when interpreting [18F]PI-2620 PET results.

Recombinant antibodies by phage display for bioanalytical applications

Antibody phage display, aimed at preparing antibodies to defined antigens, is a useful replacement for hybridoma technology. The phage system replaces all work stages that follow animal immunization with simple procedures for manipulating DNA and bacteria. It enables the time needed to generate stable antibody-producing clones to be shortened considerably, making the process noticeably cheaper. Antibodies prepared by phage display undergo several affinity selection steps and can be used as selective receptors in biosensors. This article briefly describes the techniques used in the making of phage antibodies to various antigens. The possibilities and prospects are discussed of using phage antibodies as selective agents in analytical systems, including biosensors.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Algorithmic lifestyle optimization

OBJECTIVE

A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual.

MATERIALS AND METHODS

We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group.

RESULTS

Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individual's food intolerances and allergies to 19-56 foods.

DISCUSSION

ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individual's subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints.

CONCLUSION

ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED.

Translating atherosclerosis research from bench to bedside: navigating the barriers for effective preclinical drug discovery

Cardiovascular disease (CVD) remains the leading cause of death worldwide. An ongoing challenge remains the development of novel pharmacotherapies to treat CVD, particularly atherosclerosis. Effective mechanism-informed development and translation of new drugs requires a deep understanding of the known and currently unknown biological mechanisms underpinning atherosclerosis, accompanied by optimization of traditional drug discovery approaches. Current animal models do not precisely recapitulate the pathobiology underpinning human CVD. Accordingly, a fundamental limitation in early-stage drug discovery has been the lack of consensus regarding an appropriate experimental in vivo model that can mimic human atherosclerosis. However, when coupled with a clear understanding of the specific advantages and limitations of the model employed, preclinical animal models remain a crucial component for evaluating pharmacological interventions. Within this perspective, we will provide an overview of the mechanisms and modalities of atherosclerotic drugs, including those in the preclinical and early clinical development stage. Additionally, we highlight recent preclinical models that have improved our understanding of atherosclerosis and associated clinical consequences and propose model adaptations to facilitate the development of new and effective treatments.

Wearable chemical sensors for biomarker discovery in the omics era

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies

Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the “biomarker”, and how we define a “disease”. This comes along with the shift of focus from a “one-drug-fits-all” to a “personalized approach”, placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.

In Vivo Identification of Adducts from the New Hypoxia-Activated Prodrug CP-506 Using DNA Adductomics

Many chemotherapeutic drugs exert their cytotoxicity through the formation of DNA modifications (adducts), which interfere with DNA replication, an overactive process in rapidly dividing cancer cells. Side effects from the therapy are common, however, because these drugs also affect rapidly dividing noncancerous cells. Hypoxia-activated prodrugs (HAPs) have been developed to reduce these side effects as they preferentially activate in hypoxic environments, a hallmark of solid tumors. CP-506 is a newly developed DNA-alkylating HAP designed to exert strong activity under hypoxia. The resulting CP-506-DNA adducts can be used to elucidate the cellular and molecular effects of CP-506 and its selectivity toward hypoxic conditions. In this study, we characterize the profile of adducts resulting from the reaction of CP-506 and its metabolites CP-506H and CP-506M with DNA. A total of 39 putative DNA adducts were detected in vitro using our high-resolution/accurate-mass (HRAM) liquid chromatography tandem mass spectrometry (LC-MS³) adductomics approach. Validation of these results was achieved using a novel strategy involving ¹⁵N-labeled DNA. A targeted MS/MS approach was then developed for the detection of the 39 DNA adducts in five cancer cell lines treated with CP-506 under normoxic and hypoxic conditions to evaluate the selectivity toward hypoxia. Out of the 39 DNA adducts initially identified, 15 were detected, with more adducts observed from the two reactive metabolites and in cancer cells treated under hypoxia. The presence of these adducts was then monitored in xenograft mouse models bearing MDA-MB-231, BT-474, or DMS114 tumors treated with CP-506, and a relative quantitation strategy was used to compare the adduct levels across samples. Eight adducts were detected in all xenograft models, and MDA-MB-231 showed the highest adduct levels. These results suggest that CP-506-DNA adducts can be used to better understand the mechanism of action and monitor the efficacy of CP-506 in vivo, as well as highlight a new role of DNA adductomics in supporting the clinical development of DNA-alkylating drugs.

Nanoplatforms for Sepsis Management: Rapid Detection/Warning, Pathogen Elimination and Restoring Immune Homeostasis

Sepsis, a highly life-threatening organ dysfunction caused by uncontrollable immune responses to infection, is a leading contributor to mortality in intensive care units. Sepsis-related deaths have been reported to account for 19.7% of all global deaths. However, no effective and specific therapeutic for clinical sepsis management is available due to the complex pathogenesis. Concurrently eliminating infections and restoring immune homeostasis are regarded as the core strategies to manage sepsis. Sophisticated nanoplatforms guided by supramolecular and medicinal chemistry, targeting infection and/or imbalanced immune responses, have emerged as potent tools to combat sepsis by supporting more accurate diagnosis and precision treatment. Nanoplatforms can overcome the barriers faced by clinical strategies, including delayed diagnosis, drug resistance and incapacity to manage immune disorders. Here, we present a comprehensive review highlighting the pathogenetic characteristics of sepsis and future therapeutic concepts, summarizing the progress of these well-designed nanoplatforms in sepsis management and discussing the ongoing challenges and perspectives regarding future potential therapies. Based on these state-of-the-art studies, this review will advance multidisciplinary collaboration and drive clinical translation to remedy sepsis.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Classification of Companion Diagnostics: A New Framework for Biomarker-Driven Patient Selection

Background

Modern personalized medicine strategies builds on therapy companion diagnostics to stratify patients into subgroups with differential benefit/risk. In general, stratification for drug response implies a treatment-by-subgroup interaction. This interaction is usually suggested by the drug’s mechanism of action and investigated in pharmacological research or in clinical studies. In these candidate genes or pathway approaches, either biological reasons for a differential benefit/risk or statistical interaction regarding a pharmacological or clinical endpoint or both may be given. For successful drug approval, demonstration of a positive benefit/risk balance in the intended patient population is required. This also applies to situations with biomarker-selected populations. However, further regulatory considerations relate to the usefulness and plausibility of the selected patients and benefit/risk extrapolations or alternative therapy options in biomarker-negative populations.

Methods

To facilitate the specification of regulatory requirements and support the design of clinical development programmes, a systematic classification of biomarker-drug pairs is needed, in particular with regard to the expected underlying molecular mechanism and the clinical evidence.

Results

A classification of five biomarker-drug categories is proposed related to increasing evidence on the biomarker’s predictive value in relation to a specific drug. We classified biomarkers into five ascending categories with increasing evidence on the predictive nature of the biomarker in relation to a specific drug according to the comparative pharmacological and clinical evidence.

Conclusions

The proposed classification will facilitate regulatory decision-making and support drug development with respect to biomarker-related subgrouping, both, during clinical programme and at the time of marketing authorization application, since the grade of evidence on the differential power of the biomarker can be considered as an indicator for the usefulness of a biomarker-related subgrouping.

Modular self-assembly system for development of oligomeric, highly internalizing and potent cytotoxic conjugates targeting fibroblast growth factor receptors

BACKGROUND

Overexpression of FGFR1 is observed in numerous tumors and therefore this receptor constitutes an attractive molecular target for selective cancer treatment with cytotoxic conjugates. The success of cancer therapy with cytotoxic conjugates largely relies on the precise recognition of a cancer-specific marker by a targeting molecule within the conjugate and its subsequent cellular internalization by receptor mediated endocytosis. We have recently demonstrated that efficiency and mechanism of FGFR1 internalization are governed by spatial distribution of the receptor in the plasma membrane, where clustering of FGFR1 into larger oligomers stimulated fast and highly efficient uptake of the receptor by simultaneous engagement of multiple endocytic routes. Based on these findings we aimed to develop a modular, self-assembly system for generation of oligomeric cytotoxic conjugates, capable of FGFR1 clustering, for targeting FGFR1-overproducing cancer cells.

METHODS

Engineered FGF1 was used as FGFR1-recognition molecule and tailored for enhanced stability and site-specific attachment of the cytotoxic drug. Modified streptavidin, allowing for controlled oligomerization of FGF1 variant was used for self-assembly of well-defined FGF1 oligomers of different valency and oligomeric cytotoxic conjugate. Protein biochemistry methods were applied to obtain highly pure FGF1 oligomers and the oligomeric cytotoxic conjugate. Diverse biophysical, biochemical and cell biology tests were used to evaluate FGFR1 binding, internalization and the cytotoxicity of obtained oligomers.

RESULTS

Developed multivalent FGF1 complexes are characterized by well-defined architecture, enhanced FGFR1 binding and improved cellular uptake. This successful strategy was applied to construct tetrameric cytotoxic conjugate targeting FGFR1-producing cancer cells. We have shown that enhanced affinity for the receptor and improved internalization result in a superior cytotoxicity of the tetrameric conjugate compared to the monomeric one.

CONCLUSIONS

Our data implicate that oligomerization of the targeting molecules constitutes an attractive strategy for improvement of the cytotoxicity of conjugates recognizing cancer-specific biomarkers. Importantly, the presented approach can be easily adapted for other tumor markers.

Biomarkers in Small Intestine NETs and Carcinoid Heart Disease: A Comprehensive Review

Simple Summary

Neuroendocrine tumors (NET), a heterogeneous group of tumors arising from neuroendocrine cells, often pose a diagnostic and therapeutic challenge for the clinician. Biomarkers can serve as a useful diagnostic, prognostic, and predictive tool in the management of these rare tumors. For years the field of NET biomarkers was mainly based on products se-creted by neuroendocrine tumor cells, however, during the last decade the development of nov-el multianalyte biomarkers has rapidly evolved the field. The aim of this review is to summa-rize the literature on the use and limitations of available NET biomarkers for the diagnosis and management of small intestine neuroendocrine tumors (SI-NETs) and carcinoid heart disease.

Abstract

Biomarkers remain a valuable tool for the diagnosis and management of Neuroendocrine tumors (NETs). Traditional monoanalyte biomarkers such as Chromogranin A (CgA) and 5-Hydrocyondoleacetic acid (5-HIAA) have been widely used for many years as diagnostic, predictive and prognostic biomarkers in the field of NETs. However, the clinical utility of these molecules often has limitations, mainly inherent to the heterogeneity of NETs and the fact that these tumors can often be non-secretory. The development of new molecular multianalyte biomarkers, especially the mRNA transcript based “NETest”, has rapidly evolve the field and gives the ability for a “liquid biopsy” which can reliably assess disease status in real time. In this review we discuss the use of established and novel biomarkers in the diagnosis and management of small intestine NETs and carcinoid heart disease.

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

Blending oxytocin and dopamine with everyday creativity

Converging evidence suggests that oxytocin (OT) is associated with creative thinking (CT) and that release of OT depends on ADP ribosyl-cyclases (CD38 and CD157). Neural mechanisms of CT and OT show a strong association with dopaminergic (DA) pathways, yet the link between CT and CD38, CD157, dopamine receptor D2 (DRD2) and catechol-O-methyltransferase (COMT) peripheral gene expression remain inconclusive, thus limiting our understanding of the neurobiology of CT. To address this issue, two principal domains of CT, divergent thinking (AUT), were assessed. In men, both AUT is associated with gene expression of CD38, CD157, and their interaction CD38 × CD157. There were no significant associations for DA expression (DRD2, COMT, DRD2 × COMT) on both CT measures. However, analysis of the interactions of OT and DA systems reveal significant interactions for AUT in men. The full model explained a sizable 39% of the variance in females for the total CT score. The current findings suggest that OT and DA gene expression contributed significantly to cognition and CT phenotype. This provides the first empirical foundation of a more refined understanding of the molecular landscape of CT.

Neurofilament light chain as a biomarker of meningoencephalitis of unknown etiology in dogs

BACKGROUND

Neurofilament light chain (NfL) is a neuron-specific cytoskeletal protein expressed in axons. Damaged axons of the central nervous system release NfLs into the cerebrospinal fluid (CSF) and the blood. In humans with neurologic diseases, NfL is used as a biomarker.

OBJECTIVES

To identify the potential of NfL as a supportive tool for the diagnosis, prognosis, and monitoring of meningoencephalitis of unknown etiology (MUE) in dogs.

ANIMALS

Twenty-six client-owned healthy dogs, 10 normal Beagle dogs, and 38 client-owned MUE dogs.

METHODS

Cohort study. The concentrations of NfL in serum and CSF were measured using single-molecule array technology.

RESULTS

Median NfL concentration was significantly higher in MUE dogs (serum, 125 pg/mL; CSF, 14 700 pg/mL) than in healthy dogs (serum, 11.8 pg/mL, P < .0001; CSF, 1410 pg/mL, P = .0002). The areas under the receiver operating characteristic curves of serum and CSF NfL concentrations were 0.99 and 0.95, respectively. The cut-off values were 41.5 pg/mL (serum) and 4005 pg/mL (CSF) for differentiating between healthy and MUE dogs, with sensitivities of 89.19% and 90%, respectively, and specificities of 96.97% and 100%, respectively. The NfL concentration showed a significant decrease (pretreatment, 122 pg/mL; posttreatment, 36.6 pg/mL; P = .02) in the good treatment-response group and a significant increase (pretreatment, 292.5 pg/mL; posttreatment, 1880 pg/mL, P = .03) in the poor treatment-response group.

CONCLUSIONS AND CLINICAL IMPORTANCE

Neurofilament light chain is a potential biomarker for diagnosing MUE and evaluating response to treatment.

Translational precision medicine: an industry perspective

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Magnetic resonance biomarkers in radiation oncology: The report of AAPM Task Group 294

A magnetic resonance (MR) biologic marker (biomarker) is a measurable quantitative characteristic that is an indicator of normal biological and pathogenetic processes or a response to therapeutic intervention derived from the MR imaging process. There is significant potential for MR biomarkers to facilitate personalized approaches to cancer care through more precise disease targeting by quantifying normal versus pathologic tissue function as well as toxicity to both radiation and chemotherapy. Both of which have the potential to increase the therapeutic ratio and provide earlier, more accurate monitoring of treatment response. The ongoing integration of MR into routine clinical radiation therapy (RT) planning and the development of MR guided radiation therapy systems is providing new opportunities for MR biomarkers to personalize and improve clinical outcomes. Their appropriate use, however, must be based on knowledge of the physical origin of the biomarker signal, the relationship to the underlying biological processes, and their strengths and limitations. The purpose of this report is to provide an educational resource describing MR biomarkers, the techniques used to quantify them, their strengths and weakness within the context of their application to radiation oncology so as to ensure their appropriate use and application within this field.

Causal Organic Indirect and Direct Effects: Closer to the Original Approach to Mediation Analysis, with a Product Method for Binary Mediators

Mediation analysis, which started in the mid-1980s, is used extensively by applied researchers. Indirect and direct effects are the part of a treatment effect that is mediated by a covariate and the part that is not. Subsequent work on natural indirect and direct effects provides a formal causal interpretation, based on cross-worlds counterfactuals: outcomes under treatment with the mediator set to its value without treatment. Organic indirect and direct effects avoid cross-worlds counterfactuals, using so-called organic interventions on the mediator while keeping the initial treatment fixed at treatment. Organic indirect and direct effects apply also to settings where the mediator cannot be set. In linear models where the outcome model does not have treatment-mediator interaction, both organic and natural indirect and direct effects lead to the same estimators as in the original formulation of mediation analysis. Here, we generalize organic interventions on the mediator to include interventions combined with the initial treatment fixed at no treatment. We show that the product method holds in linear models for organic indirect and direct effects relative to no treatment even if there is treatment-mediator interaction. Moreover, we find a product method for binary mediators. Furthermore, we argue that the organic indirect effect relative to no treatment is very relevant for drug development. We illustrate the benefits of our approach by estimating the organic indirect effect of curative HIV treatments mediated by two HIV persistence measures, using data on interruption of antiretroviral therapy without curative HIV treatments combined with an estimated or hypothesized effect of the curative HIV treatments on these mediators. See video abstract at http://links.lww.com/EDE/B796.

A semiparametric modeling approach for analyzing clinical biomarkers restricted to limits of detection

Before biomarkers can be used in clinical trials or patients' management, the laboratory assays that measure their levels have to go through development and analytical validation. One of the most critical performance metrics for validation of any assay is related to the minimum amount of values that can be detected and any value below this limit is referred to as below the limit of detection (LOD). Most of the existing approaches that model such biomarkers, restricted by LOD, are parametric in nature. These parametric models, however, heavily depend on the distributional assumptions, and can result in loss of precision under the model or the distributional misspecifications. Using an example from a prostate cancer clinical trial, we show how a critical relationship between serum androgen biomarker and a prognostic factor of overall survival is completely missed by the widely used parametric Tobit model. Motivated by this example, we implement a semiparametric approach, through a pseudo-value technique, that effectively captures the important relationship between the LOD restricted serum androgen and the prognostic factor. Our simulations show that the pseudo-value based semiparametric model outperforms a commonly used parametric model for modeling below LOD biomarkers by having lower mean square errors of estimation.

Parkinson's Disease Drug Development Since 1999: A Story of Repurposing and Relative Success

Background:

A global overview of drug development programs in Parkinson’s disease over the last few decades is lacking, while such programs are challenging given the multifaceted and heterogeneous nature of the disease.

Objective:

To indirectly assess drug development programs in Parkinson’s disease, exploring some factors associated with compound attrition at different trial phases.

Methods:

We assessed all Parkinson’s disease trials in the WHO trials portal, from inception (1999) to September 2019. Independent authors selected trials and extracted data. The success rate was the number of compounds that progressed to the next drug development phase divided by the number of compounds in that phase.

Results:

Overall, 357 trials (studying 152 compounds) fulfilled our inclusion criteria, with 62 (17.3%) phase 1 trials, 135 (37.8%) phase 2 trials, 85 (23.8%) phase 3 trials, and 53 (14.8%) phase 4 trials. The success rate was 42.4% from phase 2 to 3. Original compounds received regulatory approval by the FDA in 21.4% of cases, compared with 6.7% of repurposed compounds, representing an overall success rate of 14.9%. We found 172 trials (48.2%) conducted for repurposing previously licensed compounds. These figures were approximately the same regarding approval by the EMA. Most compounds were approved to treat parkinsonism and motor fluctuations.

Conclusion:

We found a moderate-to-high success rate in all phases of drug development. This was largely based on the success of original compounds, despite almost half of the identified trials attempting compound repurposing.

Quantitative Proteomics-Based Blood-Brain Barrier Study

From the viewpoint of drug discovery, it is an important issue to elucidate the drug permeability at the human central nervous system (CNS) barriers and the molecular mechanisms in the cells forming CNS barriers especially during CNS diseases. I introduced quantitative proteomics techniques into the blood-brain barrier (BBB) study, then quantitatively investigated the transport system at the human BBB and clarified the quantitative differences in protein expression levels and functions of transporters and receptors between animals and humans, or in vitro and in vivo. Based on the difference in the absolute expression level of transporters between in vitro and in vivo, I demonstrated that the drug efflux activity of P-glycoprotein (P-gp) at in vivo BBB can be accurately reconstructed from the in vitro system, not only in mouse models but also monkeys similar to humans and pathological conditions. Furthermore, I discovered Claudin-11 as another tight junction molecule expressed at the CNS barriers, and clarified that it contributes to the disruption of the CNS barriers in multiple sclerosis. Furthermore, it was also elucidated that the P-gp dysfunction causes excessive brain entry of glucocorticoid which causes a nerve damage in cerebral infarct, and it can be suppressed by targeting Abl/Src kinases. These suggest that targeting the tight junctions and transporters, which are important molecules at the CNS barriers, would potentially lead to the treatment of CNS diseases. In this review, I would like to introduce a new CNS barrier study opened by quantitative proteomics research.

Use of artificial intelligence to enhance phenotypic drug discovery

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.

Alpha7 nicotinic-N-methyl-D-aspartate hypothesis in the treatment of schizophrenia and beyond

Development of novel treatments for positive, cognitive, and negative symptoms continue to be a high-priority area of schizophrenia research and a major unmet clinical need. Given that all randomized controlled trials (RCTs) conducted to date failed with one add-on medication/mechanism of action, future RCTs with the same approach are not warranted. Even if the field develops a medication for cognition, others are still needed to treat negative and positive symptoms. Therefore, fixing one domain does not completely solve the problem. Also, targeting the cholinergic system, glutamatergic system, and cholinergic plus alpha7 nicotinic and N-methyl-D-aspartate (NMDA) receptors failed independently. Hence, targeting other less important pathophysiological mechanisms/targets is unlikely to be successful. Meta-analyses of RCTs targeting major pathophysiological mechanisms have found some efficacy signal in schizophrenia; thus, combination treatments with different mechanisms of action may enhance the efficacy signal. The objective of this article is to highlight the importance of conducting RCTs with novel combination treatments in schizophrenia to develop antischizophrenia treatments. Positive RCTs with novel combination treatments that target the alpha7 nicotinic and NMDA receptors simultaneously may lead to a disease-modifying therapeutic armamentarium in schizophrenia. Novel combination treatments that concurrently improve the three domains of psychopathology and several prognostic and theranostic biomarkers may facilitate therapeutic discovery in schizophrenia.

Simultaneous Multiplexed Detection of Protein and Metal Ions by a Colorimetric Microfluidic Paper-based Analytical Device

In order to improve the efficiency of disease diagnosis and environmental monitoring, it is desirable to detect the concentration of proteins and metal ions simultaneously, since the current popular diagnostic platform can only detect proteins or metal ions independently. In this work, we developed a colorimetric microfluidic paper-based analytical device (µPAD) for simultaneous determination of protein (bovine serum albumin, BSA) and metal ions [Fe(III) and Ni(II)]. The µPAD consisted of one central zone, ten reaction zones and ten detection zones in one device, in which reaction solutions were effectively optimized for different types of chromogenic reactions. Fe(III), Ni(II) and BSA can be easily identified by the colored products, and their concentrations are in good accordance with color depth based on the established standard curves. The detection limits are 0.1 mM for Fe(III), 0.5 mM for Ni(II) and 1µM for BSA, respectively. Best of all, we demonstrated the efficiency of the µPAD with accurate detection of Fe(III), Ni (II) and BSA from river water samples within 15 minutes. The µPAD detection is efficient, instrument-free, and easy-to-use, holding great potential for simultaneous detection of cross type analytes in numerous diagnostic fields.

Rapidly (and Successfully) Translating Novel Brain Radiotracers From Animal Research Into Clinical Use

The advent of preclinical research scanners for in vivo imaging of small animals has added confidence into the multi-step decision-making process of radiotracer discovery and development. Furthermore, it has expanded the utility of imaging techniques available to dissect clinical questions, fostering a cyclic interaction between the clinical and the preclinical worlds. Significant efforts from medicinal chemistry have also made available several high-affinity and selective compounds amenable for radiolabeling, that target different receptors, transporters and enzymes in vivo. This substantially increased the range of applications of molecular imaging using positron emission tomography (PET) or single photon emission computed tomography (SPECT). However, the process of developing novel radiotracers for in vivo imaging of the human brain is a multi-step process that has several inherent pitfalls and technical difficulties, which often hampers the successful translation of novel imaging agents from preclinical research into clinical use. In this paper, the process of radiotracer development and its relevance in brain research is discussed; as well as, its pitfalls, technical challenges and future promises. Examples of successful and unsuccessful translation of brain radiotracers will be presented.

N8-Acetylspermidine: A Polyamine Biomarker in Ischemic Cardiomyopathy With Reduced Ejection Fraction

Background Patients with ischemic cardiomyopathy (ICM) have worse outcomes than those with coronary artery disease alone and those with non-ICM. N8-acetylspermidine (N8AS) is a polyamine that regulates ischemic cardiac apoptosis and resultant cardiac dysfunction. We hypothesized that N8AS is a mechanistic biomarker of adverse outcomes in patients with ICM. Methods and Results High-resolution plasma metabolomics profiling and mass spectrometry were used to quantitate N8AS levels in a discovery cohort of 474 patients with coronary artery disease (age: 68±11 years, 12% black, 26% women): 154 with ICM, and 320 without ICM; and in an external validation cohort of 85 patients with ICM (age: 60±12 years, 37% black, 19% women). Patients without heart failure (HF) at baseline were followed for incident HF. The association between N8AS (log₂-transformed, standardized) and outcomes of all-cause mortality and incident HF were examined using Cox regression. N8AS was higher (10.39 [interquartile range, 7.21-17.75] versus 8.29 nmol/L [interquartile range, 5.91-11.42]; P<0.001) in patients with ICM compared with patients who had coronary artery disease without ICM. Higher N8AS levels were associated with higher mortality in patients with ICM (hazard ratio [HR], 1.48; 95% CI, 1.19-1.85 per SD increase [P=0.001]), independent of B-type natriuretic peptide, high-sensitivity troponin I, and high-sensitivity C-reactive protein. Findings were validated in the independent cohort. Moreover, higher N8AS level was associated with incident HF in patients without HF at baseline (HR, 4.16; 95% CI, 1.41-12.25 per SD increase [P=0.01]). Conclusions Independent of traditional HF measures, higher N8AS levels are associated with higher mortality in patients with ICM and incident HF in those who have coronary artery disease without HF. N8AS is a novel mechanistic biomarker in ICM.

Biomarkers for Pancreatic Neuroendocrine Neoplasms (PanNENs) Management-An Updated Review

Pancreatic neuroendocrine tumors (PanNENs) are rare sporadic cancers or develop as part of hereditary syndromes. PanNENs can be both functioning and non-functioning based on whether they produce bioactive peptides. Some PanNENs are well differentiated while others-poorly. Symptoms, thus, depend on both oncological and hormonal causes. PanNEN diagnosis and treatment benefit from and in some instances are guided by biomarker monitoring. However, plasmatic monoanalytes are only suggestive of PanNEN pathological status and their positivity is typically followed by deepen diagnostic analyses through imaging techniques. There is a strong need for new biomarkers and follow-up modalities aimed to improve the outcome of PanNEN patients. Liquid biopsy follow-up, i.e., sequential analysis on tumor biomarkers in body fluids offers a great potential, that need to be substantiated by additional studies focusing on the specific markers and the timing of the analyses. This review provides the most updated panorama on PanNEN biomarkers.