Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental “causal” role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a “historical” process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process — and not a static-stable condition — treatments should be tailored according to the “timing-frame” of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes — from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Health and Artificial Intelligence in the context of COVID-19 and beyond

This article introduces the main relevant aspects of ehealth or “digital health.” In this regard, by way of an introduction, the concept of health is addressed. In the second section, the core of this article, different aspects regarding the relationship between health and technology (HealthTech) are highlighted. Next, the importance of technology in order to deliver a preventive and personalized medicine, tailor-made to each patient, is addressed. Then, in the fourth section, some discriminatory situations that may arise due to not being able to access technology are discussed. Finally, I make some remarks regarding the so-called “Internet of Bodies".

Flavonoids against non-physiologic inflammation attributed to cancer initiation, development, and progression—3PM pathways

Inflammation is an essential pillar of the immune defense. On the other hand, chronic inflammation is considered a hallmark of cancer initiation and progression. Chronic inflammation demonstrates a potential to induce complex changes at molecular, cellular, and organ levels including but not restricted to the stagnation and impairment of healing processes, uncontrolled production of aggressive ROS/RNS, triggered DNA mutations and damage, compromised efficacy of the DNA repair machinery, significantly upregulated cytokine/chemokine release and associated patho-physiologic protein synthesis, activated signaling pathways involved in carcinogenesis and tumor progression, abnormal tissue remodeling, and created pre-metastatic niches, among others. The anti-inflammatory activities of flavonoids demonstrate clinically relevant potential as preventive and therapeutic agents to improve individual outcomes in diseases linked to the low-grade systemic and chronic inflammation, including cancers. To this end, flavonoids are potent modulators of pro-inflammatory gene expression being, therefore, of great interest as agents selectively suppressing molecular targets within pro-inflammatory pathways. This paper provides in-depth analysis of anti-inflammatory properties of flavonoids, highlights corresponding mechanisms and targeted molecular pathways, and proposes potential treatment models for multi-level cancer prevention in the framework of predictive, preventive, and personalized medicine (PPPM / 3PM). To this end, individualized profiling and patient stratification are essential for implementing targeted anti-inflammatory approaches. Most prominent examples are presented for the proposed application of flavonoid-conducted anti-inflammatory treatments in overall cancer management.

Protective Effects of Flavonoids Against Mitochondriopathies and Associated Pathologies: Focus on the Predictive Approach and Personalized Prevention

Multi-factorial mitochondrial damage exhibits a “vicious circle” that leads to a progression of mitochondrial dysfunction and multi-organ adverse effects. Mitochondrial impairments (mitochondriopathies) are associated with severe pathologies including but not restricted to cancers, cardiovascular diseases, and neurodegeneration. However, the type and level of cascading pathologies are highly individual. Consequently, patient stratification, risk assessment, and mitigating measures are instrumental for cost-effective individualized protection. Therefore, the paradigm shift from reactive to predictive, preventive, and personalized medicine (3PM) is unavoidable in advanced healthcare. Flavonoids demonstrate evident antioxidant and scavenging activity are of great therapeutic utility against mitochondrial damage and cascading pathologies. In the context of 3PM, this review focuses on preclinical and clinical research data evaluating the efficacy of flavonoids as a potent protector against mitochondriopathies and associated pathologies.

Targeting phytoprotection in the COVID-19-induced lung damage and associated systemic effects—the evidence-based 3PM proposition to mitigate individual risks

The risks related to the COVID-19 are multi-faceted including but by far not restricted to the following: direct health risks by poorly understood effects of COVID-19 infection, overloaded capacities of healthcare units, restricted and slowed down care of patients with non-communicable disorders such as cancer, neurologic and cardiovascular pathologies, among others; social risks—restricted and broken social contacts, isolation, professional disruption, explosion of aggression in the society, violence in the familial environment; mental risks—loneliness, helplessness, defenceless, depressions; and economic risks—slowed down industrial productivity, broken delivery chains, unemployment, bankrupted SMEs, inflation, decreased capacity of the state to perform socially important programs and to support socio-economically weak subgroups in the population. Directly or indirectly, the above listed risks will get reflected in a healthcare occupation and workload which is a tremendous long-term challenge for the healthcare capacity and robustness. The article does not pretend to provide solutions for all kind of health risks. However, it aims to present the scientific evidence of great clinical utility for primary, secondary, and tertiary care to protect affected individuals in a cost-effective manner. To this end, due to pronounced antimicrobial, antioxidant, anti-inflammatory, and antiviral properties, naturally occurring plant substances are capable to protect affected individuals against COVID-19-associated life-threatening complications such as lung damage. Furthermore, they can be highly effective, if being applied to secondary and tertiary care of noncommunicable diseases under pandemic condition. Thus, the stratification of patients evaluating specific health conditions such as sleep quality, periodontitis, smoking, chronic inflammation and diseases, metabolic disorders and obesity, vascular dysfunction, and cancers would enable effective managemenet of COVID-19-associated complications in primary, secondary, and tertiary care in the context of predictive, preventive, and personalized medicine (3PM).

Complex analysis of the personalized pharmacotherapy in the management of COVID-19 patients and suggestions for applications of predictive, preventive, and personalized medicine attitude

Aims Coronavirus disease 2019 (COVID-19) is rapidly spreading worldwide. Drug therapy is one of the major treatments, but contradictory results of clinical trials have been reported among different individuals. Furthermore, comprehensive analysis of personalized pharmacotherapy is still lacking. In this study, analyses were performed on 47 well-characterized COVID-19 drugs used in the personalized treatment of COVID-19. Methods Clinical trials with published results of drugs use for COVID-19 treatment were collected to evaluate drug efficacy. Drug-to-Drug Interactions (DDIs) were summarized and classified. Functional variations in actionable pharmacogenes were collected and systematically analysed. “Gene Score” and “Drug Score” were defined and calculated to systematically analyse ethnicity-based genetic differences, which are important for the safer use of COVID-19 drugs. Results Our results indicated that four antiviral agents (ritonavir, darunavir, daclatasvir and sofosbuvir) and three immune regulators (budesonide, colchicine and prednisone) as well as heparin and enalapril could generate the highest number of DDIs with common concomitantly utilized drugs. Eight drugs (ritonavir, daclatasvir, sofosbuvir, ribavirin, interferon alpha-2b, chloroquine, hydroxychloroquine (HCQ) and ceftriaxone had actionable pharmacogenomics (PGx) biomarkers among all ethnic groups. Fourteen drugs (ritonavir, daclatasvir, prednisone, dexamethasone, ribavirin, HCQ, ceftriaxone, zinc, interferon beta-1a, remdesivir, levofloxacin, lopinavir, human immunoglobulin G and losartan) showed significantly different pharmacogenomic characteristics in relation to the ethnic origin of the patient. Conclusion We recommend that particularly for patients with comorbidities to avoid serious DDIs, the predictive, preventive, and personalized medicine (PPPM, 3 PM) strategies have to be applied for COVID-19 treatment, and genetic tests should be performed for drugs with actionable pharmacogenes, especially in some ethnic groups with a higher frequency of functional variations, as our analysis showed. We also suggest that drugs associated with higher ethnic genetic differences should be given priority in future pharmacogenetic studies for COVID-19 management. To facilitate translation of our results into clinical practice, an approach conform with PPPM/3 PM principles was suggested. In summary, the proposed PPPM/3 PM attitude should be obligatory considered for the overall COVID-19 management.

Periodontal health related–inflammatory and metabolic profiles of patients with end-stage renal disease: potential strategy for predictive, preventive, and personalized medicine

Objectives To compare the periodontal health related–inflammatory and metabolic differences between patients with end-stage renal disease (ESRD) and healthy controls (HC), and to identify potential biomarkers in gingival crevicular fluid (GCF) and serum of ESRD patients for predictive, preventive, and personalized medicine (PPPM). Methods Patients with ESRD (ESRD group; n = 52) and healthy controls (HC group; n = 44) were recruited. Clinical periodontal parameters were recorded. The differential metabolites in the GCF and serum were identified by liquid chromatography/mass spectrometry (LC/MS). Inflammatory markers including interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), and C-reactive protein (CRP) were also assessed. Results In the ESRD group, IL-8 and CRP were significantly higher in GCF, whereas IL-6 and CRP were significantly higher in serum, compared with HC group (all P < 0.05). In the case of GCF, taurine levels were positively correlated with IL-8 levels in both groups (all P < 0.05). In the case of serum, l-phenylalanine and p-hydroxyphenylacetic acid levels were positively correlated with CRP levels in both groups (all P < 0.05). Significant positive correlations were observed between metabolites (including pseudouridine, l-phenylalanine, and p-hydroxyphenylacetic acid) and IL-6 levels only in ESRD group. Conclusions IL-8 and CRP are potential inflammatory makers that reflect the periodontal health of ESRD individual, which may be considered the valuable predictive diagnostics in the agreement with PPPM philosophy. Besides, metabolites of taurine in GCF as well as l-phenylalanine and p-hydroxyphenylacetic acid in serum are possible biomarkers correlated with inflammatory markers. All these biomarkers may also be highly recommended as a novel predictive/diagnostic tool for the assessment of inflammatory status from the perspectives of PPPM in view of susceptible population and individual screening.

Transcriptomic Characteristics of Suboptimal Health Status: Potential Utility of Novel Biomarkers for Predictive, Preventive, and Personalized Medicine Strategy

Background The early diagnosis of Suboptimal Health Status (SHS) creates a window opportunity for the predictive, preventive, and personalized medicine (PPPM) of chronic diseases. Previous studies have observed the alterations in several mRNA levels in SHS individuals. As a promising “omics” technology offering comprehension of genome structure and function at RNA level, transcriptome profiling can provide innovative molecular biomarkers for the predictive diagnosis and targeted prevention of SHS. Methods To explore the potential diagnostic biomarkers, biological functions, and signaling pathways involved in SHS, an RNA sequencing (RNA-Seq)-based transcriptome analysis was firstly conducted on buffy coat samples collected from 30 participants with SHS and 30 age- and sex-matched healthy controls. Results Transcriptome analysis identified a total of 46 differentially expressed genes, in which 22 transcripts were significantly increased and 24 transcripts were decreased in the SHS group. A total of 23 transcripts was selected as candidate diagnostic biomarkers for SHS. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that several biological processes were related to SHS, such as ATP-binding cassette (ABC) transporter and neurodegeneration. Protein-protein interaction (PPI) network analysis identified 10 hub gene related to SHS, including GJA1, TWIST2, KRT1, TUBB3, AMHR2, BMP10, MT3, BMPER, NTM and TMEM98. A transcriptome diagnosis model can distinguish SHS individuals from the healthy controls with a sensitivity of 83.3% (95% Confidence Interval (CI): 73.9%-92.7%), a specificity of 90.0% (95% CI: 82.4%-97.6%), and an area under the receiver operating characteristic curve of 0.938 (95% CI: 0.882–0.994). Conclusion Blood transcripts are potentially objective biomarkers for the SHS diagnosis. These findings determine the potential utility of SHS-related transcriptomic biomarkers for PPPM of chronic diseases.

Inflammatory and metabolic profiles of patients with end-stage renal disease: potential strategy for predictive, preventive, and personalized medicine

Objectives Few studies reported the periodontal disease-related metabolic profile of end-stage renal disease (ESRD) patients. The present study aimed to compare the inflammatory and metabolic differences between patients with ESRD and healthy controls, and to identify potential useful biomarkers for predictive, preventive, and personalized medicine (PPPM) in GCP and serum of ESRD patients.Methods Patients with ESRD (ESRD group; n = 52) and healthy controls (HC group; n = 44) were recruited. Clinical periodontal parameters were recorded. The differential metabolites in the GCF and serum were identified by liquid chromatography/mass spectrometry. Inflammatory markers including Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Interleukin-8 (IL-8) and C-reactive protein (CRP) were also assessed. Results In ESRD group, IL-8 and CRP were significantly higher in GCF, whereas IL-6 and CRP were significantly higher in serum, compared with HC group (all P < 0.05). In the case of GCF, taurine levels were positively correlated with IL-8 levels in both groups (all P < 0.05). In the case of serum, L-phenylalanine and p-hydroxyphenylacetic acid levels were positively correlated with CRP levels in both groups (all P < 0.05). Significant positive correlation was observed between pseudouridine and IL-6 levels only in ESRD group. Conclusions IL-8 and CRP were potential inflammatory makers. Metabolites of taurine in GCF as well as L-phenylalanine and p-hydroxyphenylacetic acid in serum were possible biomarkers that correlated with inflammatory cytokine. All these biomarkers may consider as a potential strategy for the prediction, diagnosis, prognosis, and management of personalized periodontal therapy in the population with ESRD.