In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.

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Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant inter-population pharmacogenomic biomarker allele frequency differences, particularly in 7 clinically actionable pharmacogenomic biomarkers in 7 European populations, affecting drug efficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on the differences observed in the prevalence of high-risk genotypes in these populations, as far as common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMT pharmacogenes are concerned. Also, our data demonstrate notable differences in predicted genotype-based warfarin dosing among these populations. Our findings can be exploited not only to develop guidelines for medical prioritization, but most importantly to facilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomic testing. This may subsequently contribute towards significant cost-savings in the overall healthcare expenditure in the participating countries, where pharmacogenomics implementation proves to be cost-effective.

During pregnancy, cell-free DNA (cfDNA) in maternal blood encompasses a small percentage of cell-free fetal DNA (cffDNA), an easily accessible source for determination of fetal disease status in risk families through non-invasive procedures. In case of monogenic heritable disease, background maternal cfDNA prohibits direct observation of the maternally inherited allele. Non-invasive prenatal diagnostics (NIPD) of monogenic diseases therefore relies on parental haplotyping and statistical assessment of inherited alleles from cffDNA, techniques currently unavailable for routine clinical practice. Here, we present monogenic NIPD (MG-NIPD), which requires a blood sample from both parents, for targeted locus amplification (TLA)-based phasing of heterozygous variants selectively at a gene of interest. Capture probes-based targeted sequencing of cfDNA from the pregnant mother and a tailored statistical analysis enables predicting fetal gene inheritance. MG-NIPD was validated for 18 pregnancies, focusing on CFTR, CYP21A2, and HBB. In all cases we could predict the inherited alleles with >98% confidence, even at relatively early stages (8 weeks) of pregnancy. This prediction and the accuracy of parental haplotyping was confirmed by sequencing of fetal material obtained by parallel invasive procedures. MG-NIPD is a robust method that requires standard instrumentation and can be implemented in any clinic to provide families carrying a severe monogenic disease with a prenatal diagnostic test based on a simple blood draw.

Peptide and protein drugs have evolved in recent years into mainstream therapeutics, representing a significant portion of the pharmaceutical market. Peptides and proteins exhibit highly diverse structures, broad biological activities as hormones, neurotransmitters, structural proteins, metabolic modulators and therefore have a significant role as both therapeutics and biomarkers. Understanding the metabolism of synthetic or biotechnologically derived peptide and protein drugs is critical for pharmaceutical development as metabolism has a significant impact on drug efficacy and safety. Although the same principles of pharmacokinetics and metabolism of small molecule drugs apply to peptide and protein drugs, there are few notable differences. Moreover, the study of peptide and protein drug metabolism is a rather complicated process which requires sophisticated analytical techniques, and mass spectrometry based approaches have provided the capabilities for efficient and reliable quantification, characterization, and metabolite identification. This review article will focus on the current use of mass spectrometry for the study of the metabolism of peptide and protein drugs.

Overall, our data demonstrate that whole-genome sequencing, unlike conventional genetic screening methods, is necessary to determine an individual's pharmacogenomics profile in a more comprehensive manner, which, combined with the gradually decreasing whole-genome sequencing costs, would expedite bringing personalized medicine closer to reality.

IntroductionThe objective of this study was to evaluate the efficacy of intravenous (i.v.) injection of liposomally encapsulated dexamethasone phosphate (DxM-P) in comparison to free DxM-P in rats with established adjuvant arthritis (AA). This study focused on polyethylene glycol (PEG)-free liposomes, to minimize known allergic reactions caused by neutral PEG-modified (PEG-ylated) liposomes.MethodsEfficacy was assessed clinically and histologically using standard scores. Non-specific and specific immune parameters were monitored. Activation of peritoneal macrophages was analyzed via cytokine profiling. Pharmacokinetics/biodistribution of DxM in plasma, synovial membrane, spleen and liver were assessed via mass spectrometry.ResultsLiposomal DxM-P (3 × 1 mg/kg body weight; administered intravenously (i.v.) on Days 14, 15 and 16 of AA) suppressed established AA, including histological signs, erythrocyte sedimentation rate, white blood cell count, circulating anti-mycobacterial IgG, and production of interleukin-1beta (IL-1β) and IL-6 by peritoneal macrophages. The suppression was strong and long-lasting. The clinical effects of liposomal DxM-P were dose-dependent for dosages between 0.01 and 1.0 mg/kg. Single administration of 1 mg/kg liposomal DxM-P and 3 × 1 mg/kg of free DxM-P showed comparable effects consisting of a partial and transient suppression. Moreover, the effects of medium-dose liposomal DxM-P (3 × 0.1 mg/kg) were equal (in the short term) or superior (in the long term) to those of high-dose free DxM-P (3 × 1 mg/kg), suggesting a potential dose reduction by a factor between 3 and 10 by liposomal encapsulation. For at least 48 hours after the last injection, the liposomal drug achieved significantly higher levels in plasma, synovial membrane, spleen and liver than the free drug.ConclusionsThis new PEG-free formulation of macrophage-targeting liposomal DxM-P considerably reduces the dose and/or frequency required to treat AA, with a potential to enhance or prolong therapeutic efficacy and limit side-effects also in the therapy of rheumatoid arthritis. Depot and/or recirculation effects in plasma, inflamed joint, liver, and spleen may contribute to this superiority of liposomally encapsulated DxM-P.

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disorder. Two forms are recognized, familial (FALS) that accounts for 5–10% of ALS cases, and sporadic (SALS) that accounts for the rest. Early diagnosis of ALS is important because it improves their therapeutic efficacy. Current diagnosis is based on clinical assessment and requires approximately 12 months, leading to a significant delay in drug administration. Therefore, new methods are required for the earlier diagnosis of ALS. Screening for pathogenic variants in known ALS‐associated genes is already exploited as a diagnostic tool in ALS but cannot be applied for population‐based screening. New circulating biomarkers (proteins or small molecules) are needed for initial screening, whereas specific diagnostic methods can be applied to confirm the presence of pathogenic variants in the selected population subgroup. Lipids appear as promising biomarkers for population‐based screening and for monitoring disease progression. Genetic analysis can also assist in the prediction of disease progression by analyzing disease‐modifying genes, for example, EPHA4 and CHGB. Furthermore, molecular diagnosis will aid the stratification of ALS patients for improved pharmacological approaches. Here, we discuss current and novel diagnostic strategies and how they can be applied to revolutionize the field of ALS molecular diagnosis.