Measuring Effectiveness of Treatments in Rare Disorders, Evidence from Clinical Trials in Fragile X Syndrome

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Measuring Effectiveness of Treatments in Rare Disorders, Evidence from Clinical Trials in Fragile X Syndrome

https://doi.org/10.21203/rs.3.rs-3439158/v1

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Fragile X Syndrome (FXS) is the leading cause of inherited intellectual disability and autism spectrum disorder (ASD). It results from mutations of the FMR1 gene and the subsequent loss of Fragile X mental disorder 1 protein (FMRP). FXS has been the focus of intense preclinical research leading to several clinical trials. Effect sizes compare the effectiveness of treatments for neurodevelopmental disorders, such as FXS. This paper reviews effect sizes of different drug treatments in clinical trials for FXS to understand potential issues with the designs of current trials which could affect efficacy detectability. We searched for "Fragile X Syndrome" query in PubMed and filtered the clinical trials and downloaded the list of all publication IDs. PubMed's API was then utilized to collect the abstract's content into a spreadsheet. Studies were narrowed down to those with sufficient statistics data to calculate effect size relative to placebo. Phase 2 and 3 clinical trials for arbaclofen and one trial for L-acetylcarnitine reported large effect sizes. No significant findings were present between placebo and treatment groups. Studies without a placebo group reported larger treatment effect sizes. Small sample sizes, scarcity of studies, and outcome measures based on caregiver reports prevented us from drawing conclusions.

Biological sciences/Drug discovery/Drug regulation

Biological sciences/Psychology/Human behaviour

Health sciences/Health care/Drug regulation

Health sciences/Health care/Health services

Health sciences/Health care/Public health

Health sciences/Medical research/Study design

Rare disorders (RD) represent a group of diverse conditions defined by a global prevalence of 40 per 100,000 people¹. Together, over 7000 conditions have been identified as RD impacting 0.04% of the global population¹. RD leads to significant effects on affected individuals, their families, and society. The development of treatment for RD has proven especially challenging, considering their low prevalence and medical complexity. So, even with strong preclinical data, testing interventions is difficult with the current model. A large number of RD are associated with neurodevelopmental disability (NDD), such as autism spectrum disorder (ASD) and intellectual disability (ID).

Fragile X syndrome (FXS) is one RD but is also the leading form of inherited ID and ASD^2,3. Individuals with FXS present with physical features, including a long face, prominent ears, flexible fingers, and macroorchidism⁴. which has allowed its diagnosis prior to the identification of the gene. We now know that individuals can present with variable phenotypes as well. FXS is the result of excessive repeats of the CGG motif in the FMR1 gene that silences the production of the Fragile X mental disorders 1 protein (FMRP)⁵. FXS is often associated with other NDD, such as attention-deficit/hyperactivity disorder (ADHD), motor coordination deficits, as well as related conditions: anxiety disorders and social/communication issues. FXS has been at the forefront of research, which has translated in the last 2 decades into multiple clinical trials⁶. This can be a good model for other NDDs.

Unfortunately, there is still no approved mechanistic treatment for FXS⁷. So, we sought to understand if we could compare previous trials for their relative efficacy. This is important as several researchers have discussed potentially combining drug treatments. We therefore wanted to assess the effect sizes of each drug as a measure of efficacy. Effect sizes can be used in clinical trials to compare the effectiveness of two different treatments by comparing the size of the difference between treatments⁸. Additionally, effect sizes can be used in a placebo-controlled clinical trial to compare the effectiveness of a single treatment by comparing the difference between a placebo control group and a treatment group. For example, effect sizes can be used to compare the efficacy of different treatments on patients with autism and other neurodevelopmental disorders, such as ADHD, generalized anxiety disorder, and Fragile X syndrome. For example, one meta-analysis (of 6 trials) studying the efficacy of the stimulant methylphenidate to treat adult ADHD found a mean effect size of 0.9, which was statistically significant⁹. Another meta-analysis of 22 studies examining the efficacy of risperidone in children with autism spectrum disorders, with most sample sizes being around 30 patients, reported a mean effect size of 1.047¹⁰. Another literature review of 45 studies with a mean sample size of 97 found a mean effect size of 0.9 for risperidone in treating behavioral disturbances in youth with below-average IQ and conduct disorder¹¹. Another study examining the effectiveness of various treatments (such as SSRI’s, SNRI’s, benzodiazepines, azapirones, and antihistamines) using double-blind placebo-controlled trials for generalized anxiety disorder, with most sample sizes being around 300 people reported a mean effect size of 0.36 for SSRIs¹². One last meta-analysis, using 12 placebo-controlled trials with a mean sample size of 145, found a mean effect size of 0.55 for selective serotonin reuptake inhibitors (SSRIs) relative to placebo in treating panic disorder¹³.

Effect size measures the magnitude of the difference between treatment groups, with effect size being calculated by dividing the difference in the means of two populations by the standard deviation (SD)¹⁴. This study used the pooled standard deviation to calculate the effect size. Cohen's d is a standardized effect size used between two groups of similar standard deviations and sizes. Cohen set different criteria for small (d = 0.2), medium (d = 0.5), and large (d ≥ 0.8) effect sizes¹⁵. When statistical significance is reached, it does not necessarily indicate that a significant effect has occurred. Instead, the magnitude of the effect size is of interest, revealing how much of a change has occurred¹⁶.

While an ideal treatment would involve either modifying the impaired genetic mechanisms of the FMR1 gene or rescuing of FMRP production, there are currently no large-scale clinical trials that have managed to address these issues. A previous review concluded that they could not recommend any pharmacological treatments for patients with FXS¹⁷. Instead, the focus has been on prescribing treatments that can potentially ameliorate cognitive and behavioral deficits, with many candidate treatments for FXS symptoms being drugs already being used in the treatment of other mental/ID disorders.

Placebo groups in Fragile X syndrome randomized-controlled trials should be accounted for as significant placebo effect sizes have been found in caregiver-rated outcome measures, with older individuals responding better to placebo treatments¹⁸. Efforts should be taken to mitigate the placebo effect as it does seem to play a role in common outcome measures in Fragile X clinical trials.

The objective of this review is to identify and assess the effectiveness of treatments in clinical trials for Fragile X Syndrome. By comparing the outcome measures used in various clinical trials, this review aims to identify the most promising treatments by identifying treatments with significant effect sizes. The goal of this review was to review the current literature on FXS treatments and to evaluate whether there were any promising treatments with significant or large effect sizes while accounting for the placebo effect. Additionally, this paper aims to assess the importance of placebo groups in clinical trials.

We conducted a PubMed search using the query "Fragile X syndrome" to locate peer-reviewed clinical trials, then refined the results by filtering for clinical trials and downloading a list of publication IDs. Then PubMed's API was used to collect the abstract's content into a spreadsheet for ease of sorting. Additionally, we searched Ebase, PsycINFO, and Central using the “Fragile X Syndrome” search queue and then applied a filter to find clinical trials. In total, 219 unique studies were queried through this process.

We then set strict eligibility criteria for acceptable studies (Fig. 1). First, one criterion required for inclusion in this review was that the studies needed to be clinical trials with a placebo group and a treatment group. As our goal was to derive the effect size of the drug and placebo on the outcome measures, the publication needed to include the following: the standard deviation (SD), mean, and sample size values for every pre and post-trial outcome measure for both the placebo and treatment groups. This data also allowed us to calculate the 95% confidence intervals, as suggested by Lee¹⁹. Out of the 219 total studies, 170 did not have usable data to be analyzed. Out of the remaining 49 studies, 40 were removed due to missing data (e.g., placebo groups). In other words, there were 9 remaining studies that contained all the necessary data and overlapping outcome measures that could be compared. Although those 40 clinical trials did not contain placebo groups, 13 of these 40 studies had large effect sizes with overlapping outcome measures that may warrant further investigation.

Common outcome measures between the 9 studies containing completely sufficient data studies were extracted (Table 1), with those being scores related to Aberrant Behavior Checklist (ABC) total and subscales (specifically the Lethargy, Hyperactivity, Irritability/Agitation elements, Inappropriate Speech, and Stereotypical Behavior), Clinical Global Improvement severity scale (CGI-S), Conners 3 Parent Rating Scale (specifically the subscales of Hyperactivity and Inattention), and Vineland (specifically the socialization, communication, and maladaptive behavior subscales). Forest plots with each overlapping outcome measure and corresponding effect sizes are included for each of the 9 studies. The direction of the effect size was adjusted to reflect an improvement or decline in each outcome measure.

Table 1

FXS Clinical Trial Studies with Overlapping Outcome Measures Used for Effect Size Calculations.
Study	Drug/Treatment	Population	Participants	Sample Size	Outcome Measures
De La Torre (Study 6) (²⁰)	Epigallocatechin-3-gallate	Fragile X	Adults	12	ABC subscales
Bruno (Study 8) (²¹)	Donepezil	Fragile X	Anyone with FXS	12	ABC total, ABC subscales
Hessel (Study 11) (²²)	Cogmed	Fragile X	Children and adolescents	49	Conners 3 Parent Rating Scale
Youssef (Study 15) (²³)	Basimglurant	Fragile X	Adolescents and Adults	25	CGI-S, ABC Total
Sahu (Study 35) (²⁴)	Donepezil	Fragile X	Boys	10	Conners 3 Parent Rating Scale
Berry-Kravis (Study 86) (²⁵)	CX516	Fragile X	Adult Males and Females aged 18–50	24	ABC subscales
Veenstra-VanderWeele(Study 98) (²⁶)	Arbaclofen	Fragile X	Age 5–21	69	ABC subscales, Vineland socialization, CGI-S
Berry-Kravis (Study 112) (²⁷)	Arbaclofen	Fragile X	Adolescents and adults	62	ABC subscales, Vineland socialization, CGI-S
Torrioli(Study 147) (²⁸)	L-acetylcarnitine	Fragile X	Boys	24	Vineland socialization, Conners 3 Parent Rating Scale (Hyperactivity)

The most commonly used outcome measure was the ABC, which was found to be used in 5/9 studies (Fig. 2A-E), which also reported the numbers required for an effect size calculation. The two Arbaclofen studies interestingly had the largest positive treatment effect sizes on all the ABC subscales. Although the majority of effect sizes reported were not considered large according to Cohen, a large effect size of 0.822 for the ABC lethargy outcome measure was reported for patients undergoing placebo treatment, with a smaller effect size for the actual arbaclofen treatment (0.740). These two arbaclofen studies had a mildly positive effect on all 5 subscales of the ABC (ES Lethargy: 0.740 (study 112), 0.525 (study 98), hyperactivity: 0.545 (112), 0.371 (98), Irritability: 0.339 (112), 0.322 (98), Stereotypical Behaviour: 0.605 (112), 0.233 (98), Inappropriate Speech: 0.456 (112), 0.211 (98)). But there was also a similar mildly positive effect on all 5 subscales for placebo treatment (ES Lethargy: 0.822 (112), 0.687 (98), hyperactivity: 0.454 (112), 0.300 (98), Irritability: 0.411 (112), 0.315 (98), Stereotypical Behaviour: 0.433 (112), 0.362 (98), Inappropriate Speech: 0.529 (112), 0.234 (98)). Despite the mild effect sizes, no significant findings were found between the placebo and treatment groups. The phase 3 arbaclofen study was promising as it suggested improvements for younger patients, despite not seeing improvements in primary endpoints²⁷.

The CX516 study appeared to have small effect sizes for all the ABC subscales²⁵. In the case of epigallocatechin-3-gallate (EP3G), we noted that the drug (treatment group) had the opposite effect on inappropriate speech compared to the rest of the ABC subscales. A mildly positive effect size was reported for EP3G on all 5 subscales of ABC except for inappropriate speech, where a weakly negative effect size was reported (-0.132). The negative effect size reported for the inappropriate speech subscale suggests that these symptoms worsened for the EP3G treatment group (-0.132). However, the rest of the ABC subscales suggest an improvement in symptoms as reflected by positive effect sizes for EP3G.

The placebo group for basimglurant was interesting as it reported the largest positive effect size for the ABC Total outcome measure (0.844). Unexpectedly, the effect size was larger for the placebo group than the treatment groups for both 0.5mg (0.465) and 1.5 mg of basimglurant (0.573). Donepezil reported the smallest effect sizes for the placebo and treatment groups on the ABC Total measure. Because the confidence intervals overlap, no significant findings were found.

Next, we found that the Vineland socialization subscale was used by 3/9 (Fig. 3) studies reporting measures required for ES calculation.

Looking at the Vineland 2 subscales, the 3 studies reported relatively small effect sizes. However, arbaclofen showed some promising results as mildly positive effect sizes were reported on all 3 of the subscales, with the exception of the phase 3 clinical trial on the maladaptive behaviour subscale (where the largest negative effect size of -0.452 was found). However, the negative effect size suggests that arbaclofen tended to worsen maladaptive behaviour. A mildly positive effect size was found for L-acetylcarnitine (0.379) on the socialization subscale, with smaller positive effect sizes being found for the 2 arbaclofen studies. However, because the confidence intervals overlap, no significant findings were found.

Another commonly used scale is the Clinical Global Impression Severity Scale (CGI-S) to report clinical improvements from multiple domains related to the condition. This was used by 3/9 studies (Fig. 4).

Again, the 2 arbaclofen studies are promising as they both reported mildly positive treatment effect sizes (0.634 and 0.469), which were greater than both basimulgrant doses (0.367 and 0.337). However, all of the placebo effect sizes for the 3 studies were very similar (0.316, 0.333 and 0.332). It is important to note that there was a significant difference between the treatment and placebo groups for Arbaclofen phase 2 as determined by an unpaired t-test. However, a phase 3 clinical trial failed to reproduce a significant effect between the placebo and treatment groups²⁷.

Next, we found that the Conners’ Parent Rating Scale was used by 3/9 (Fig. 5) studies reporting measures required for ES calculation.

L-acetylcarnitine showed promising results as the treatment group reported the largest effect size on the Conners 3 Parent Rating subscale for hyperactivity (1.16), representing a reduction in hyperactivity symptoms. However, it is unclear if this large effect size is valid, as the reporting of the quantitative data may have been poor in the study. Unfortunately, the study did not measure inattention. Donepezil also showed promising results, as the treatment group reported a mildly positive effect size for hyperactivity (0.433) but a smaller effect size for inattention (0.0955). It is also interesting that the placebo group produced a larger effect size (0.772) than the treatment group for inattention (0.0955), speaking to the large placebo effect. In addition, a Cogmed intervention reported a small positive effect size for both the treatment and placebo groups for both hyperactivity and inattention.

Studies that did not include a placebo group but contained only a treatment group with overlapping outcome measures were graphed (Table 2). In particular, the ABC subscales (Fig. 6) and CGI-S (Fig. 7) outcome measures were graphed. In these studies without a placebo group, a larger effect size was observed compared to those with placebo groups.

Table 2

FXS clinical trial studies with overlapping outcome measures but without placebo groups used for effect size calculations.
Study	Drug/Treatment	Population	Participants	Sample Size	Outcome Measures
Heussler (Study 7) (²⁹)	Transdermal cannabidiol	Fragile X	Children	18	ABC subscales
Caku (Study 24) (³⁰)	Lovastatin	Fragile X	Anyone with FXS	15	ABC subscales
Schneider (Study 31) (³¹)	Minocycline	Fragile X	Children	5	CGI-S
Erickson (Study 34) (³²)	Acamprosate	Fragile X	Youth	12	ABC subscales
Erickson (Study 41) (³³)	Aripiprazole	Fragile X	Boys	10	ABC subscales
Erickson (Study 42) (³⁴)	Riluzole	Fragile X	Adults	6	ABC subscales
Paribello (Study 44) (³⁵)	Minocycline	Fragile X	Ages 13–32	20	ABC subscales, CGI-S
Torrioli (Study 45) (³⁶)	Valproic Acid	Fragile X	Boys	10	Conners Parent Rating, CGI-S
Kesler (Study 48) (³⁷)	Donepezil 5mg and 10 mg	Fragile X	Males	8	ABC subscales
Berry-Kravis (Study 49) (³⁸)	Lithium	Fragile X	Ages 6–23	15	ABC subscales, CGI-S
Erickson (Study 97) (³⁹)	Arbaclofen	Fragile X	Children and adolescents	32	ABC subscales, CGI-S
Biag (Study 106) (⁴⁰)	Metformin	Fragile X	Children	9	ABC subscales
Erickson (Study 121) (⁴¹)	Memantine	Fragile X	Ages 13–22	6	CGI-S

One interesting finding worth mentioning is that aripiprazole reported large effect sizes for multiple ABC subscales. However, as this study did not contain a placebo group that can be compared to, it is difficult to determine its true efficacy. Future clinical trials should include a placebo group to investigate aripiprazole’s true efficacy.

Another interesting finding is that lovastatin reported a weakly negative effect size for ABC hyperactivity (-0.06), indicating a worsening of hyperactivity symptoms in some subjects.

Another promising finding is that two different minocycline studies reported very large effect sizes for the CGI-S outcome measure (2.83 and 3.60). However, both of these studies did not include a placebo group that could be used for comparison. Given these limitations, further clinical trials that include a placebo group with sufficient data are required.

This review of the effect size in clinical trials for FXS recapitulated that there has been no trial-proven mechanistic treatment for FXS so far. Interestingly, we found several potential ways in which trials could be designed to improve our ability to capture efficacy in the future.

First, we found that not all the studies reported all the necessary data required to calculate the effect size. This is amplified by a shortage of studies on Fragile X, given that it is a rare disease. We suggest that journals require investigators to provide a complete list, including the standard deviation, mean, and sample sizes of pre and post-trial outcome measures.

Another major issue is that most studies contain small sample sizes, resulting in larger 95% confidence intervals (CI). As a consequence, some placebo-controlled studies may not show a difference, especially when there is a smaller effect size (type II error). As most drugs are likely to have a small effect size, a larger sample size will be key to reducing the CI and showing superiority to placebo.

We also found limited overlap between studies in the outcome measures used. This makes it hard to compare between studies conducted over time. A consensus would, therefore, be important in order to show how some drugs are superior to others.

Our study also illustrated how the choice of outcome measures may affect our ability to detect changes compared to placebo. Indeed, clinical trials involving outcome measures based on caregiver reports are often affected by significant placebo effects¹⁸. The placebo effect was, in most studies, comparable to the investigational product effect, suggesting either lack of efficacy of the drug or inflation in the placebo group. It is interesting to note that many studies reported higher placebo effect sizes compared to treatment effect sizes. So, we would suggest that while considering patient and family-reported outcomes is important, objective measures may be essential in preventing a placebo response to obscure treatment effects. We also found no overlap in studies using outcome measures that can capture a wide range of symptoms, reproduce results, and correlate with quality-of-life improvements⁴².

Similarly, we assessed if studies that did not include a placebo group presented within the same range of effect size as those that did include a placebo. We found that studies including placebo groups tended to have lower effect sizes. For instance, a study using donepezil 5 mg and donepezil 10 mg reported effect sizes of 1.05 and 1.23 on the ABC Irritability subscale when there was no placebo group, respectively, but only an effect size of 0.242 on the same subscale when a placebo group was included.

Given the importance of larger clinical trials and the inclusion of placebo trials, it will be important for clinicians and families to work together for research in RD and NDD. Our results also support developing patient-relevant outcome measures which will be less affected by the placebo effect.

Acknowledgements:

Not applicable.

Authors Contributions:

AV co-wrote the manuscript and assisted in designing the graphs. AV also organized the data and calculated the effect sizes and 95% confidence intervals in the spreadsheet. AV also designed and created the tables. AM co-wrote the manuscript and created and designed the graphs as well as substantively revised and edited the manuscript. TL designed and also created figures. TL also assisted in organizing the studies and assisted in writing the manuscript. MK conducted the literature search by searching for, extracting, and sorting the raw data. FVB designed the project, supervised the review of literature and co-wrote the manuscript.

Availability of Data and Materials:

The datasets used and analyzed during the current study are available from the PubMed repository, https://pubmed.ncbi.nlm.nih.gov/.

Additional Information:

The authors declare that they do not have any competing interests.

Ethics Approval and Consent to Participate:

This project included analysis of published data.

Consent for Publication:

Not Applicable.

Funding:

This project was not directly funded. FVB receives funding from the Canadian Institutes of Health Research (CIHR).

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No competing interests reported.

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