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Ultra-precision medicine | Nature Biotechnology

A Comment in this issue describes n-Lorem, an ambitious not-for-profit initiative with a mission to discover individualized experimental antisense oligonucleotide (ASO) treatments for patients with ultra-rare conditions arising from a single, often de novo, mutation (for example, an indel, repeat or single point mutation). Since its founding last year, n-Lorem has built a network of public–private partnerships with Ionis Pharmaceuticals, Biogen, Ultragenyx, Charles River Laboratories, Covance, the Korea Institute of Toxicology and the US National Institutes of Health’s Undiagnosed Disease Network to discover and develop individualized ASO drugs collaboratively with clinical investigators in academic medical centers.

My Happiness vs The Pandemic. In Spreadsheets.

I have been tracking my mood every day throughout this pandemic. I use an app that I released myself called Changes. It lets me write a little diary entry and rate my mood and add hashtags. I’ll analyse this data and see what actually happened. I’ll be using Google Sheets to help you try the same with your data in future.

N-of-1 Trials: Evidence-Based Clinical Care or Medical Research that Requires IRB Approval? A Practical Flowchart Based on an Ethical Framework. - PubMed - NCBI

N-of-1 trials can provide high-class evidence on drug treatment effectiveness at the individual patient level and have been given renewed interest over the past decade due to improvements of the initial single patient design. Despite these recent developments, there is still no consensus under what circumstances N-of-1 trials should be considered as part of evidence-based clinical care and when they represent medical research with need for institutional review board (IRB) approval. This lack of consensus forms an obstacle for a more widespread implementation of N-of-1 trials. Based upon the existing literature, we as a group of researchers involved in N-of-1 trials and members of the IRB of a tertiary academic referral center, designed a practical flowchart based on an ethical framework to help make this distinction. The ethical framework together with a practical flowchart are presented in this communication.

Tackling statin intolerance with n-of-1 trials (TaSINI) in primary care: protocol for a feasibility randomised trial to increase statin adherence. - PubMed - NCBI

INTRODUCTION: Statins reduce the incidence of cardiovascular disease (CVD) and cause few adverse effects. Half of patients prescribed statins discontinue treatment due to perceived intolerance. Placebo-controlled (blinded) n-of-1 trials have shown people with perceived intolerance that the statin does not cause adverse events and most resume treatment. However, blinded n-of-1 trials are impractical to deliver in routine practice. Tackling Statin Intolerance using n-of-1 trials (TaSINI) will test the feasibility of a general practitioner (GP)-delivered behavioural intervention endorsing an unblinded n-of-1 trial to increase adherence to statins relative to usual care.

"Asking Too Much?": Randomized N-of-1 Trial Exploring Patient Preferences and Measurement Reactivity to Frequent Use of Remote Multidimensional Pai... - PubMed - NCBI

Once-a-day pain reporting provides rich contextual information. Although patients were less adherent to this preferred sampling strategy, once-a-day reporting still provides more frequent assessment opportunities compared with other less intense or overburdensome schedules.

Some t-tests for N-of-1 trials with serial correlation

However, researchers conducting N-of-1 trials seem to prefer simpler analysis methods. Gabler et al. (2011) reviewed analyses conducted in 108 N-of-1 trials and found 52% used visual analysis, 44% used t-tests, and 24% used nonparametric methods (some studies used more than one analysis method) [3]. Punja et al. (2016) reviewed 100 reports of conducted (60%) and planned (40%) N-of-1 trials [13]. Seventy-five of these performed or planned statistical analyses: 53% of these 75 used paired t-tests and 32% used a nonparametric method. Though several of these simple analysis methods use only the observations from one individual, they fail to account for serial correlation. A substantial proportion of researchers using N-of-1 trials sacrifice their need for appropriate analyses to their desire for simplicity. Our goal in this work is to tend to their analytical needs and desires by developing a simple method that uses only the data from a single individual.

Some t-tests for N-of-1 trials with serial correlation

This work develops a formula-based statistical method for N-of-1 studies that accounts for serial correlation while using only the data from a single individual to draw inferences. Most existing methods emerged with increases in computing power. These methods typically provide inference on two types of differences between two treatments: level- and rate-change. Level-change is when the difference in means is not dependent on the time series of the treatments, whereas rate-change is when the difference in means is dependent on the time series of the treatments. Rochon (1990) describes a large-sample, maximum likelihood method that evaluates both level- and rate-change, but no closed-form estimator exists [8]. Hence, an iterative procedure produces the estimates. McKnight et al. (2000) developed a double-bootstrap method for making inference on level- and rate-change [9]. Their first bootstrap estimates serial correlation; the second uses the estimated correlation to compare two treatments. They provide statistical properties for their method, and they focus on trials having as few as 20 or 30 observations. Borckardt and company describe statistical properties of the Simulation Modelling Analysis for N-of-1 trials, and consider trials having between 16 and 28 observations from an individual [10, 11]. Simulation Modelling Analysis is similar to a parametric bootstrap method, with the bootstrap method generating replicates under the null hypothesis. Empirical p-values for level- and rate-change result. Lin et al. (2016) propose semiparametric and parametric bootstrap methods (only one bootstrap needed) for evaluating level- and rate-change [12]. They explore the statistical properties of their method for trials having 28 observations. Other N-of-1 methods exist, but the methods described here are the only ones we could find that use only the observations from a single individual and account for serial correlation.

Scientists bring personalized medicine to the biology of aging - STAT

“Of course the whole body ages,” said biologist Michael Snyder, who led the study. “But in a given individual, some systems age faster or slower than others. One person is a cardio-ager, another is a metabolic ager, another is an immune ager,” as shown by changes over time in nearly 100 key molecules that play a role in those systems. “There is quite a bit of difference in how individuals experience aging on a molecular level.” Crucially, the molecular markers of aging do not necessarily cause clinical symptoms. The study’s “immune” agers had no immune dysfunction; “liver agers” did not have liver disease. Everyone was basically healthy. If aging is truly personal, understanding an individual’s ageotype could lead to individualized, targeted intervention. “We think [ageotypes] can show what’s going off track the most so you can focus on that if you want to affect your aging,” Snyder said.

Uric acid: What’s optimal? – Michael Lustgarten

Lycopene is found almost exclusively in tomatoes and watermelon. If these foods are related to my increasing levels of uric acid, if I ate less of them, I’d expect to see a corresponding decrease in uric acid. So, in 2019, I ate less of these foods, thereby reducing my average lycopene intake from 11,585 to 9,132 micrograms per day. How did that affect circulating levels of uric acid? In 6 measurements for 2019, my average uric acid level was 4.6 mg/dL, a value that was significantly different (p=0.02) from the 2016-2018 average of 5.2 mg/dL. Whether eating less watermelon and tomatoes caused the decrease is unknown, but it’s good to know that uric acid can be potentially modified with dietary change!

Need to control blood sugar? There's a drink for that: Ketone supplement may control glucose by mimicking some aspects of a ketogenic diet -- ScienceDaily

"There is mounting evidence that a low carbohydrate ketogenic diet is very effective in controlling blood sugar and even reversing Type 2 diabetes," says Little. "We wanted to know what would happen if artificial ketones were given to those with obesity and at risk for Type 2 diabetes but who haven't been dieting." To test the idea, Little and his team asked 15 people to consume a ketone drink after fasting overnight. After 30 minutes, they were then asked to drink a fluid containing 75 grams of sugar while blood samples were taken. "It turns out that the ketone drink seemed to launch participants into a sort of pseudo-ketogenic state where they were better able to control their blood sugar levels with no changes to their insulin," explains Little. "It demonstrates that these supplements may have real potential as a valuable tool for those with Type 2 diabetes."

A Randomized Clinical Trial of n-of-1 Trials—Tribulations of a Trial | Research, Methods, Statistics | JAMA Internal Medicine | JAMA Network

Given the lack of evidence in their favor and their inconvenience for patients and physicians, the burden of proof now firmly rests with their proponents. With everything said, we empathize with our colleagues, but for now this may represent another instance of a beautiful idea being vanquished by cruel and ugly evidence.

So you want to make a personalized drug? This company has got your back

Charles River’s role is largely in conducting toxicology and dose-finding studies, providing regulatory expertise, and perhaps most importantly, pushing the limits of what a trimmed-down investigational new drug application can look like. These N-of-1 projects are insignificant to Charles River’s bottom line, for now. “The work they are doing for us is discounted and for a customer they hope they never see again,” Rich says.

N-of-1 (Single-Patient) Trials for Statin-Related Myalgia | Annals of Internal Medicine | American College of Physicians

Eight patients (mean age, 66 years [SD, 8 years]; 88% women, all with high 10-year Framingham cardiovascular risk) participated in n-of-1 trials. Seven patients completed 3 treatment pairs, and 1 completed 2 treatment pairs. For each n-of-1 trial, no statistically significant differences were seen between statin and placebo in the VAS myalgia score, symptom-specific VAS score, pain interference score, and pain severity score. Five patients resumed open-label statin treatment, with a median posttrial follow-up of 10 months.

Capsule Commentary on Odineal et al., Effect of Mobile Device-Assisted N-of-1 Trial Participation on Analgesic Prescribing for Chronic Pain: Randomized Controlled Trial | SpringerLink

In this study, Odineal and colleagues1 examined changes in prescription analgesic prescribing for approximately 200 patients with chronic pain randomized to either a mobile app–enabled N-of-1 study (tailored, individualized pain-control interventions) or a control group. The app allowed patients to choose two treatment plans to compare over several short trials, selecting from a list of commonly prescribed analgesics or non-pharmaceutical therapies such as yoga or physical therapy. Among intervention patients, the authors found a clinically and statistically significant decrease in NSAID prescriptions relative to controls. Nearly one-quarter of intervention patients stopped NSAIDs during the study period, and the between-group difference was also significant.

N-of-1 Trials: FDA Plots Path to Regulation | RAPS

“At the very least, during the time needed to discover and develop an intervention, quantifiable, objective measures of the patient’s disease status should be identified and tracked, since, in an N-of-one experiment, evaluation of disease trends before and after treatment will usually be the primary method of assessing effectiveness,” Woodcock and Marks explained.

Living a longer, healthier life: A systems approach to medicine at WesternU's Pumerantz Lecture | Benzinga

"We think that clinical trials in the future ought to be done as N-of-1 (single subject) experiments," Hood said. "In a cancer trial we can use the individual data clouds to actually identify biomarkers that distinguish the responders from the non-responders. Then what we will do is a second trial of 50 patients with all responders. And if you get a 98 percent response rate, the FDA will approve your drug in the blink of an eye. You go from spending $1.5 billion on a clinical trial to spending hundreds of thousands of dollars on a clinical trial." Hood and his collaborators completed a study that used dynamic data clouds to improve wellness. "A wellness study of 108 individuals using personal, dense, dynamic data clouds," published in Nature Biotechnology in 2017, involved collecting the complete genome sequences of 108 volunteers, including Hood. The subjects each gave blood draws every three months to measure 1,200 analytes of three classes: clinical chemistries, metabolites and proteins. They measured gut microbiome every three months and used Fitbit and other devices for digital health measurements. "What these gave us for each individual was longitudinal data clouds that, when analyzed, led to actionable possibilities that if executed by the individual could either improve their wellness and/or let them ameliorate or avoid disease," Hood said. "The big question in all of this is: How do you change social behavior? What we used were wellness coaches, trained in psychology and nutrition and nursing, who were magnificent. They would elicit from the individual exactly what they wanted in their health objectives, and this is not easy to do."

The 'pathobiome' -- a new understanding of disease -- ScienceDaily

The concept acknowledges that all organisms are in fact complex communities of viruses, microbes and other small organisms (e.g. parasites) which can interact to affect health or disease status at any given time. These complex communities continually interact with their hosts, sometimes conferring benefits (e.g. "good" bacteria in the human gut microbiome), and at other times causing harm by contributing to disease. When these communities combine to cause disease they are termed "pathobiomes" -- a recognition of their collective shift away from the healthy-state "symbiome."

Pragmatic trials revisited: applicability is about individualization - Journal of Clinical Epidemiology

Classically, clinical research has centered on studying groups of individuals to extrapolate the findings to the general population. It is time to walk back from the population (the average patient) to the individual patient, understanding that population-oriented research is actually exploratory and individual-oriented research is confirmatory [24].

Pragmatic trials revisited: applicability is about individualization - Journal of Clinical Epidemiology

These designs represent an important step toward stratified therapy, but N-of-1 trials [14] are the purest form of pragmatic patient-centered design [15]. N-of-1 trials are multiple-period, crossover experiments comparing two or more treatments within individual patients. They are the optimal design to estimate individual treatment effects directly and to identify the best treatment for each individual patient in each specific setting. The Journal of Clinical Epidemiology has recently published a number of articles reviewing the main features and applications of N-of-1 trials [16].

Educate Your Patients…or They Will Take Medical Advice From Their Hairdresser |

“One of the bigger distractions in sports medicine practices is that patients often focus on what we do with professional athletes…everyone wants to try what worked for Kobe Bryant. But I tell them that is an n of 1, and what they should truly be basing their decision on is the result of a large prospective study where you are looking at efficacy of a specific dosage and formulary, for their particular type of orthopaedic problem. And this is our job to present that data in a fair fashion, particularly because of the appearance of conflict involved in these cash-based procedures that are rarely covered by insurance.  “Because medicine has become a consumer field we must focus on public education. If we were to poll the physicians who are performing most of these treatments, they will likely agree that the evidence is still pending but looking promising, and furthermore that the patients are asking for it.” I spend a good amount of time in my clinic talking to these patients about the current evidence (and lack of such) behind these treatments, and some still do choose to move forward with this option.

Statistical considerations for rare diseases drug development. - PubMed - NCBI

One of the most challenges for rare disease clinical trials is probably the availability of a small patient population. It is then a great concern on how to conduct clinical trials with a small number of subjects available for obtaining substantial evidence regarding safety and effectiveness for approval of the rare disease drug product under investigation. FDA, however, does not have the intention to create a statutory standard for approval of orphan drugs that are different from the standard for approval of drugs in common conditions. Thus, it is suggested that innovative trial designs such as a complete n-of-1 trial design or an adaptive design should be used for an accurate and reliable assessment of rare disease drug products under investigation. In this article, basic considerations, innovative trial designs, and statistical methods for data analysis are discussed. In addition, some innovative thinking for the evaluation of rare disease drug products is proposed.

Cost savings due to n-of-1

Omeprazole was the appropriate treatment in only 52% of these chronic users of acid-suppressing drugs. Eleven of 27 trials (41%) indicated that ranitidine was the preferred treatment. The SPT method proved acceptable to patients, feasible to administer, and reproducible. It can statistically discriminate effectiveness and adverse events and serve as a useful, prognostic tool in community practice by determining the least costly, evidence-based, appropriate treatment.

Industrial n-of-1

The N-of-1 trials propose replacing large-scale trials of whole groups with methodical study of individual patients. However, the requirement to provide specific treatment to different subgroups of patients will make clinical trials more complex, so the industry needs to redesign how it interacts with patients. CROs will need to establish expert teams to structure and run precision-medicine-oriented trials for their sponsor clients.