The right dose for every patient: a key step for precision medicine. Nat Rev Drug Discov. 2016;15:145-146. [PMID: 26669674 DOI: 10.1038/nrd.2015.22]

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

Binder jetting 3D printing in fabricating pharmaceutical solid products for precision medicine

Current treatment strategies are moving towards patient-centricity, which emphasizes the need for new solutions allowing for medication tailored to a patient. This can be realized by precision medicine where patient diversity is considered during treatment. However, the broader use of precision medicine is restricted by the current technological solutions and rigid manufacturing of pharmaceutical products by mass production principles. Additive manufacturing of pharmaceutical products can provide a feasible solution to this challenge. In this review, a particular subtype of additive manufacturing, that is, binder jetting 3D printing, is introduced as a solution for fabricating pharmaceutical solid products that can be considered as precision medicine. Technical aspects, practical applications, unique advantages and challenges related to this technique are discussed, indicating that binder jetting 3D printing possesses the potential for fabricating already new product prototypes, where diversity in patient treatment in terms of the needs for specific drug type, dose and drug release can be accounted. To further advance this type of mass customization of pharmaceuticals, multidisciplinary research initiatives are needed not only to cover the engineering aspects but also to bridge these innovations with patient-centric perspectives.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling

Drug-induced cardiotoxicity (DICT) is a leading cause of drug trial failure and discontinuation. Current drug annotations for cardiotoxicity largely focus on individual outcomes or mechanisms. Considering the broad spectrum of adverse cardiac events, we developed Drug-Induced Cardiotoxicity Rank (DICTrank) using FDA labeling and comprehensively classified 1318 human drugs into four categories: Most-DICT-Concern (n = 341), Less-DICT-Concern (n = 528), No-DICT-Concern (n = 343), and Ambiguous-DICT-Concern (n = 106). Notably, DICTrank covers diverse therapeutic categories, of which several were enriched with Most-DICT-Concern drugs, such as antineoplastic agents, sex hormones, anti-inflammatory drugs, beta-blockers, and cardiac therapy. DICTrank currently presents the largest drug list of DICT annotation, and it could contribute to the development of new approach methods, including AI models for early identification of DICT risk during drug development and beyond.

Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.

Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study

In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC0-24 , AUC24-48 , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUCSS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC24-48 , although the AUCSS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.

Characterization of behavioral changes in T-maze alternation from dopamine D1 agonists with different receptor coupling mechanisms

RATIONALE

Dopamine D1 receptor agonists have been shown to improve working memory, but often have a non-monotonic (inverted-U) dose-response curve. One hypothesis is that this may reflect dose-dependent differential engagement of D1 signaling pathways, a mechanism termed functional selectivity or signaling bias.

OBJECTIVES AND METHODS

To test this hypothesis, we compared two D1 ligands with different signaling biases in a rodent T-maze alternation task. Both tested ligands (2-methyldihydrexidine and CY208243) have high intrinsic activity at cAMP signaling, but the former also has markedly higher intrinsic activity at D1-mediated recruitment of β-arrestin. The spatial working memory was assessed via the alternation behavior in the T-maze where the alternate choice rate quantified the quality of the memory and the duration prior to making a choice represented the decision latency.

RESULTS

Both D1 drugs changed the alternate rate and the choice latency in a dose-dependent manner, albeit with important differences. 2-Methyldihydrexidine was somewhat less potent but caused a more homogeneous improvement than CY208243 in spatial working memory. The maximum changes in the alternate rate and the choice latency tended to occur at different doses for both drugs.

CONCLUSIONS

These data suggest that D1 signaling bias in these two pathways (cAMP vs β-arrestin) has complex effects on cognitive processes as assessed by T-maze alternation. Understanding these mechanisms should allow the identification or discovery of D1 agonists that can provide superior cognitive enhancement.

Drug dosage modifications in 24 million in-patient prescriptions covering eight years: A Danish population-wide study of polypharmacy

Polypharmacy has generally been assessed by raw counts of different drugs administered concomitantly to the same patients; not with respect to the likelihood of dosage-adjustments. To address this aspect of polypharmacy, the objective of the present study was to identify co-medications associated with more frequent dosage adjustments. The data foundation was electronic health records from 3.2 million inpatient admissions at Danish hospitals (2008-2016). The likelihood of dosage-adjustments when two drugs were administered concomitantly were computed using Bayesian logistic regressions. We identified 3,993 co-medication pairs that associate significantly with dosage changes when administered together. Of these pairs, 2,412 (60%) did associate with readmission, mortality or longer stays, while 308 (8%) associated with reduced kidney function. In comparison to co-medications pairs that were previously classified as drug-drug interactions, pairs not classified as drug-drug interactions had higher odds ratios of dosage modifications than drug pairs with an established interaction. Drug pairs not corresponding to known drug-drug interactions while still being associated significantly with dosage changes were prescribed to fewer patients and mentioned more rarely together in the literature. We hypothesize that some of these pairs could be associated with yet to be discovered interactions as they may be harder to identify in smaller-scale studies.

How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action

Background

Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized.

Discussion

There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery.

Conclusion

We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Identification of heptapeptides targeting a lethal bacterial strain in septic mice through an integrative approach

Effectively killing pathogenic bacteria is key for the treatment of sepsis. Although various anti-infective drugs have been used for the treatment of sepsis, the therapeutic effect is largely limited by the lack of a specific bacterium-targeting delivery system. This study aimed to develop antibacterial peptides that specifically target pathogenic bacteria for the treatment of sepsis. The lethal bacterial strain Escherichia coli MSI001 was isolated from mice of a cecal ligation and puncture (CLP) model and was used as a target to screen bacterial binding heptapeptides through an integrative bioinformatics approach based on phage display technology and high-throughput sequencing (HTS). Heptapeptides binding to E. coli MSI001 with high affinity were acquired after normalization by the heptapeptide frequency of the library. A representative heptapeptide VTKLGSL (VTK) was selected for fusion with the antibacterial peptide LL-37 to construct the specific-targeting antibacterial peptide VTK-LL37. We found that, in comparison with LL37, VTK-LL37 showed prominent bacteriostatic activity and an inhibitive effect on biofilm formation in vitro. In vivo experiments demonstrated that VTK-LL37 significantly inhibited bacterial growth, reduced HMGB1 expression, alleviated lesions of vital organs and improved the survival of mice subjected to CLP modeling. Furthermore, membrane DEGP and DEGQ were identified as VTK-binding proteins by proteomic methods. This study provides a novel strategy for targeted pathogen killing, which is helpful for the treatment of sepsis in the era of precise medicine.

Better governance starts with better words: why responsible human tissue research demands a change of language

The rise of precision medicine has led to an unprecedented focus on human biological material in biomedical research. In addition, rapid advances in stem cell technology, regenerative medicine and synthetic biology are leading to more complex human tissue structures and new applications with tremendous potential for medicine. While promising, these developments also raise several ethical and practical challenges which have been the subject of extensive academic debate. These debates have led to increasing calls for longitudinal governance arrangements between tissue providers and biobanks that go beyond the initial moment of obtaining consent, such as closer involvement of tissue providers in what happens to their tissue, and more active participatory approaches to the governance of biobanks. However, in spite of these calls, such measures are being adopted slowly in practice, and there remains a strong tendency to focus on the consent procedure as the tool for addressing the ethical challenges of contemporary biobanking. In this paper, we argue that one of the barriers to this transition is the dominant language pervading the field of human tissue research, in which the provision of tissue is phrased as a 'donation' or 'gift', and tissue providers are referred to as 'donors'. Because of the performative qualities of language, the effect of using 'donation' and 'donor' shapes a professional culture in which biobank participants are perceived as passive providers of tissue free from further considerations or entitlements. This hampers the kind of participatory approaches to governance that are deemed necessary to adequately address the ethical challenges currently faced in human tissue research. Rather than reinforcing this idea through language, we need to pave the way for the kind of participatory approaches to governance that are being extensively argued for by starting with the appropriate terminology.

A dopamine D1 agonist vs. methylphenidate in modulating prefrontal cortical working memory

Methylphenidate is used widely to treat symptoms of attention-deficit/hyperactivity disorder (ADHD), but like other stimulants has significant side effects. This study utilized a rodent model (spontaneously hypertensive rat) of spatial working memory (sWM) to compare the effects of methylphenidate with the novel dopamine D₁-like receptor agonist 2-methyldihydrexidine. Acute oral administration of methylphenidate (1.5 mg/kg) caused sWM improvement in half of the tested rats, but impairment in the others. Both improvement or impairment were eliminated by administration of the D₁ antagonist SCH39266 directly into the prefrontal cortex (PFC). Conversely, 2-methyldihydrexidine showed greater sWM improvement compared to methylphenidate without significant impairment in any subject. Its effects correlated negatively with vehicle-treated baseline performance (i.e., rats with lower baseline performance improved more than rats with higher baseline performance). These behavioral effects were associated with neural activities in the PFC. Single neuron firing rate was changed, leading to the alteration in neuronal preference to correct or error behavioral responses. Overall, 2-methyldihydrexidine was superior to methylphenidate in decreasing the neuronal preference, prospectively, in the animals whose behavior was improved. In contrast, methylphenidate, but not 2-methyldihydrexidine, significantly decreased neuronal preference, retrospectively, in those animals who had impaired performance. These results suggest that a D₁ agonist may be more effective than methylphenidate in regulating sWM-related behavior through neural modulation of the PFC, and thus may be superior to methylphenidate or other stimulants as ADHD pharmacotherapy. Significance Statement Methylphenidate is effective in ADHD by its indirect agonist stimulation of dopamine and/or adrenergic receptors, but the precise effects on specific targets are unclear. We compared methylphenidate to a dopamine D₁ receptor-selective agonist by investigating effects on working memory occurring via neural modulation in the prefrontal cortex. The data suggest that pharmacological treatment selectively targeting the dopamine D₁ may offer a superior approach to ADHD pharmacotherapy.

Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study

Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.

Identifying Dose Components of Manual Acupuncture to Determine the Dose-Response Relationship of Acupuncture Treatment: A Systematic Review

The dose-response relationship is a hallmark of pharmacological studies. However, this relationship has not been fully established in acupuncture research. This systematic review aims to provide the characteristics of the dose-response relationship in acupuncture research. We further summarized the differences in acupuncture effects according to dose components. Dose components of acupuncture were categorized into three groups: number of needles, stimulation intensity, and total number/frequency of treatments. The PubMed database was used to identify studies examining the effects of different doses of acupuncture from the establishment of the database to August 13, 2020. Dose components and responses were extracted from each study, and the results of low- and high-dose conditions were compared. Fourteen studies were included in this study. Of the included studies, 37.5% showed statistically significant enhanced responses to acupuncture treatment under high-dose conditions compared to low-dose conditions. Significant differences between high- and low-dose conditions were observed most frequently in studies that used various stimulation intensities (four out of six studies), followed in order by studies that used various numbers of needles (two out of seven studies), and those that used various numbers or frequencies of treatment (none of the three studies). Responses were categorized into symptom changes, physiological changes, experimentally induced pain/stimuli perception, and needling sensation. Stimulation intensity, which is considered one of the most important needling components, might indeed have a great impact on clinical responses to acupuncture.

Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology

Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed dosing tables or are based on maximum a posteriori estimates. These approaches, however, lack a quantification of uncertainty and/or consider only part of the available patient-specific information. We propose three novel approaches for MIPD using Bayesian data assimilation (DA) and/or reinforcement learning (RL) to control neutropenia, the major dose-limiting side effect in anticancer chemotherapy. These approaches have the potential to substantially reduce the incidence of life-threatening grade 4 and subtherapeutic grade 0 neutropenia compared with existing approaches. We further show that RL allows to gain further insights by identifying patient factors that drive dose decisions. Due to its flexibility, the proposed combined DA-RL approach can easily be extended to integrate multiple end points or patient-reported outcomes, thereby promising important benefits for future personalized therapies.

A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study

Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

Spatial, Temporal, and Dose Control of Drug Delivery using Noninvasive Magnetic Stimulation

Noninvasive stimuli-responsive drug delivery using magnetic fields in conjunction with superparamagnetic nanoparticles offers the potential for the spatial and temporal control of drug release. When hyperthermia is not desired and control of the dosage is required, it is necessary to design a platform in which local heating on the nanoscale releases the therapeutic cargo without the bulk heating of the surrounding medium. In this paper, we report a design using a stimuli-responsive nanoparticle platform to control the dosage of the cargo released by an alternating magnetic field (AMF) actuation. A core@shell structure with a superparamagnetic doped iron oxide (MnFe₂O₄@CoFe₂O₄) nanoparticle core in a mesoporous silica shell was synthesized. The core used here has a high saturation magnetization value and a high specific loss power for heat generation under an AMF. The mesoporous shell has a high cargo-carrying capacity. A thermoresponsive molecular-based gatekeeper containing an aliphatic azo group was modified on the core@shell nanoparticles to regulate the cargo release. The mesoporous structure of the silica shell remained intact after exposure to an AMF, showing that the release of cargo is due to the removal of the gatekeepers instead of the destruction of the structure. Most importantly, we demonstrated that the amount of cargo released could be adjusted by the AMF exposure time. By applying multiple sequential exposures of AMF, we were able to release the cargo step-wise and increase the total amount of released cargo. In vitro studies showed that the death of pancreatic cancer cells treated by drug-loaded nanoparticles was controlled by different lengths of AMF exposure time due to different amount of drugs released from the carriers. The strategy developed here holds great promise for achieving the dosage, temporal, and spatial control of therapeutics delivery without the risk of overheating the particles' surroundings.

Engineering Precision Medicine

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs-on-chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Personalized Integrated Network Modeling of the Cancer Proteome Atlas

Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks, infer patient-specific networks and elicit interpretable pathway-level signatures. PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings ( https://mjha.shinyapps.io/PRECISE/ ).

Drug-Induced Rhabdomyolysis Atlas (DIRA) for idiosyncratic adverse drug reaction management

Drug-induced rhabdomyolysis (DIR) is an idiosyncratic and fatal adverse drug reaction (ADR) characterized in severe muscle injuries accompanied by multiple-organ failure. Limited knowledge regarding the pathophysiology of rhabdomyolysis is the main obstacle to developing early biomarkers and prevention strategies. Given the lack of a centralized data resource to curate, organize, and standardize widespread DIR information, here we present a Drug-Induced Rhabdomyolysis Atlas (DIRA) that provides DIR-related information, including: a classification scheme for DIR based on drug labeling information; postmarketing surveillance data of DIR; and DIR drug property information. To elucidate the utility of DIRA, we used precision dosing, concomitant use of DIR drugs, and predictive modeling development to exemplify strategies for idiosyncratic ADR (IADR) management.

Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those therapeutics should be optimized based on patient-specific pharmacokinetic/pharmacodynamic properties. Under the current approach to treatment response planning and assessment, there does not exist an efficient method to consolidate biomarker changes into a holistic understanding of treatment response. While the majority of research on chemotherapies focus on cellular and genetic mechanisms of resistance, there are numerous patient-specific and tumor-specific measures that contribute to treatment response. New approaches that consolidate multimodal information into actionable data are needed. Mathematical modeling offers a solution to this problem. In this perspective, we first focus on the particular case of breast cancer to highlight how mathematical models have shaped the current approaches to treatment. Then we compare chemotherapy to radiation therapy. Finally, we identify opportunities to improve chemotherapy treatments using the model of radiation therapy. We posit that mathematical models can improve the application of anticancer therapeutics in the era of precision medicine. By highlighting a number of historical examples of the contributions of mathematical models to cancer therapy, we hope that this contribution serves to engage investigators who may not have previously considered how mathematical modeling can provide real insights into breast cancer therapy.

Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation

AIM

The aim of the present study was to predict olanzapine (OLZ) exposure in individual patients using physiologically based pharmacokinetic modelling and simulation (PBPK M&S).

METHODS

A 'bottom-up' PBPK model for OLZ was constructed in Simcyp® (V14.1) and validated against pharmacokinetic studies and data from therapeutic drug monitoring (TDM). The physiological, demographic and genetic attributes of the 'healthy volunteer population' file in Simcyp® were then individualized to create 'virtual twins' of 14 patients. The predicted systemic exposure of OLZ in virtual twins was compared with measured concentration in corresponding patients. Predicted exposures were used to calculate a hypothetical decrease in exposure variability after OLZ dose adjustment.

RESULTS

The pharmacokinetic parameters of OLZ from single-dose studies were accurately predicted in healthy Caucasians [mean-fold errors (MFEs) ranged from 0.68 to 1.14], healthy Chinese (MFEs 0.82 to 1.18) and geriatric Caucasians (MFEs 0.55 to 1.30). Cumulative frequency plots of trough OLZ concentration were comparable between the virtual population and patients in a TDM database. After creating virtual twins in Simcyp®, the R2 values for predicted vs. observed trough OLZ concentrations were 0.833 for the full cohort of 14 patients and 0.884 for the 7 patients who had additional cytochrome P450 2C8 genotyping. The variability in OLZ exposure following hypothetical dose adjustment guided by PBPK M&S was twofold lower compared with a fixed-dose regimen - coefficient of variation values were 0.18 and 0.37, respectively.

CONCLUSIONS

Olanzapine exposure in individual patients was predicted using PBPK M&S. Repurposing of available PBPK M&S platforms is an option for model-informed precision dosing and requires further study to examine clinical potential.

The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients

Glioma is often diagnosed at a later stage, and the high risk of recurrence remains a major challenge. We hypothesized that the microRNA expression profile may serve as a biomarker for the prognosis and prediction of glioblastoma recurrence. We defined microRNAs that were associated with good and poor prognosis in 300 specimens of glioblastoma from the Cancer Genome Atlas. By analyzing microarray gene expression data and clinical information from three random groups, we identified 7 microRNAs that have prognostic and prognostic accuracy: microRNA-124a, microRNA-129, microRNA-139, microRNA-15b, microRNA-21, microRNA-218 and microRNA-7. The differential expression of these miRNAs was verified using an independent set of glioma samples from the Affiliated People's Hospital of Jiangsu University. We used the log-rank test and the Kaplan-Meier method to estimate correlations between the miRNA signature and disease-free survival/overall survival. Using the LASSO model, we observed a uniform significant difference in disease-free survival and overall survival between patients with high-risk and low-risk miRNA signature scores. Furthermore, the prognostic capability of the seven-miRNA signature was demonstrated by receiver operator characteristic curve analysis. A Circos plot was generated to examine the network of genes and pathways predicted to be targeted by the seven-miRNA signature. The seven-miRNA-based classifier should be useful in the stratification and individualized management of patients with glioma.

Breast cancer: The translation of big genomic data to cancer precision medicine

Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single-gene to whole-genome screening by using genome-wide association study and next-generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high-throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative "-omics" data to refine the current medical practice to be applied in the near future.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

Sex-Divergent Clinical Outcomes and Precision Medicine: An Important New Role for Institutional Review Boards and Research Ethics Committees

The efforts toward individualized medicine have constantly increased in an attempt to improve treatment options. These efforts have led to the development of small molecules which target specific molecular pathways involved in cancer progression. We have reviewed preclinical studies of sunitinib that incorporate sex as a covariate to explore possible sex-based differences in pharmacokinetics and drug–drug interactions (DDI) to attempt a relationship with published clinical outputs. We observed that covariate sex is lacking in most clinical outcome reports and suggest a series of ethic-based proposals to improve research activities and identify relevant different sex outcomes. We propose a deeper integration of preclinical, clinical, and translational research addressing statistical and clinical significance jointly; to embed specific sex-divergent endpoints to evaluate possible gender differences objectively during all stages of research; to pay greater attention to sex-divergent outcomes in polypharmacy scenarios, DDI and bioequivalence studies; the clear reporting of preclinical and clinical findings regarding sex-divergent outcomes; as well as to encourage the active role of scientists and the pharmaceutical industry to foster a new scientific culture through their research programs, practice, and participation in editorial boards and Institutional Ethics Review Boards (IRBs) and Research Ethics Committees (RECs). We establish the IRB/REC as the centerpiece for the implementation of these proposals. We suggest the expansion of its competence to follow up clinical trials to ensure that sex differences are addressed and recognized; to engage in data monitoring committees to improve clinical research cooperation and ethically address those potential clinical outcome differences between male and female patients to analyze their social and clinical implications in research and healthcare policies.

Healthcare innovation and patent law's 'pharmaceutical privilege': is there a pharmaceutical privilege? And if so, should we remove it?

This article reviews current trends in patent claims regarding personalised, stratified and precision medicine. These trends are not particularly well understood by policymakers, even less by the public, and are quite recent. Consequently, their implications for the public interest have hardly been thought out. Some see personalised and other secondary drug patent claims as promoting better targeted treatment. Others are inclined to see them as \manifestations of 'evergreening' whereby companies are, in some cases quite cynically, trying to extend market monopolies in old products or creating new monopolies based on supposedly improved versions of such earlier drugs. The article claims that the relaxation of 'novelty' is a privilege unavailable to inventions in other fields and that on balance the patent system does privilege this industry and that no adequate case has yet been made thus far to prove the public benefits overall.

Orodispersible films: Towards drug delivery in special populations

Orodispersible films (ODF) hold promise as a novel delivery method, with the potential to deliver tailored therapies to different patient populations. This article reviews the current strides of ODF technology and some of its unmet quality and manufacturing aspects. A topic highlights opportunities and limitations of inkjet printed ODF as a population-specific drug delivery. Overall, this article aims to stimulate further research to fill the current knowledge gap between manufacturing and administration requirements of ODF targeting specific patient subpopulations such as geriatrics.

CYP2D6 Genetic Variation and Beta-Blocker Maintenance Dose in Patients with Heart Failure

PURPOSE

This study examined whether a CYP2D6 polymorphism (CYP2D6*4) was related to beta-blocker maintenance dose in patients with heart failure.

METHODS

Logistic regression modeling was utilized in a retrospective chart-review analysis of heart-failure patients (60% Male, 90% of European descent) to assess whether CYP2D6*4 (non-functional CYP2D6 allele present in 1 of 5 individuals of European descent) is associated with maintenance dose of carvedilol (n = 65) or metoprolol (n = 33).

RESULTS

CYP2D6*4 was associated with lower maintenance dose of metoprolol (OR 0.13 [95% CI 0.02-0.75] p = 0.023), and a trend was observed between CYP2D6*4 and higher maintenance dose of carvedilol (OR 2.94 [95% CI 0.84-10.30] p = 0.093). None of the patients that carried CYP2D6*4 achieved the recommended target dose of metoprolol (200 mg/day).

CONCLUSION

Consistent with the role of CYP2D6 in the metabolism of metoprolol, the tolerated maintenance dose of metoprolol was lower in CYP2D6*4 carriers compared to non-carriers. Consistent with the role of CYP2D6 in activation of carvedilol, tolerated maintenance dose of carvedilol was higher in CYP2D6*4 carriers compared to non-carriers. Further investigation is warranted to ascertain the potential of CYP2D6 as a potential predictive biomarker of beta-blocker maintenance dose in heart failure patients.

Role of precision medicine in pancreatic cancer

Pancreatic cancer is one of the most challenging problems in modern oncology. Due to difficultly in early diagnosis and early distant metastasis of pancreatic cancer, surgical resection rate is less than 20% and patients' prognosis is very poor. Despite long-term efforts taken to develop treatments for pancreatic cancer, the survival rate did not significantly improve. Therefore, early diagnosis and treatment are the key to improve the survival rate of patients with pancreatic cancer. The advent of big-data genomic era and the rapid development of biotechnology have led to the recent proposal of a new concept of precise medicine, which has quickly become the focus of world medical conferences. Here, we describe the new progress and challenges of precision medicine in pancreatic cancer, with an aim to provide new ideas for improving the survival rate of patients with pancreatic cancer.