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. 2019 Oct 18;2019(10):CD013457. doi: 10.1002/14651858.CD013457

D‐cycloserine for autism spectrum disorder

Swe Zin Aye 1,✉, Han Ni 2, Htwe Htwe Sein 1, San Thidar Mon 3, Qishi Zheng 4, Yoko Kin Yoke Wong 4
PMCID: PMC6797547

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the effectiveness and adverse effects of D‐cycloserine for children, adolescents, and adults with autistic spectrum disorder (ASD).

Background

Description of the condition

'Autism' is derived from the Greek word autos, which means 'self', and the term was first used by the German psychiatrist Eugen Bleuler in 1911 to describe social withdrawal in people with schizophrenia (Moskowitz 2011). The child psychiatrist Dr Leo Kanner described infantile autism, a lifelong neurodevelopmental condition, in 1943 (Kanner 1943). However, the first two editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) labelled it as a type of schizophrenia occurring in childhood (DSM; DSM‐II). It was not until 1980 that infantile autism was officially recognised as a separate, stand‐alone category in the DSM‐III. When the DSM‐IV was published in 1994, people could be diagnosed with four pervasive developmental disorders (PDD): autistic disorders; Asperger’s disorder; childhood disintegrative disorder; and PDD–not otherwise specified. Recently, the DSM‐5 has subsumed all four PDD under the umbrella term 'autism spectrum disorder' (ASD). This classification is in line with the 10th revision of the International Classification of Diseases (ICD‐10).

The definition of ASD comprises five criteria in the DSM‐5, and the ICD‐11:

  • social communication and social interaction impairment;

  • restricted, repetitive, stereotyped patterns of behaviour and interests;

  • symptoms must be present in the early developmental period;

  • symptoms cause clinically significant impairment in current functioning; and

  • these disturbances are not better explained by intellectual disability or global developmental delay.

Prevalence

In 2014, the prevalence of ASD in 8‐year‐olds in the USA was 16.8 per 1000, or 1 in 59 (Baio 2018). Brugha 2011 reported a prevalence among UK adults as 1 in 100. Kim 2011 reported that 2.64% of 7‐ to 12‐year‐olds in a South Korean community had ASD. It is estimated that 1 in 160 children worldwide are currently diagnosed with ASD (WHO 2018). The prevalence of ASD has been increasing in recent decades, due to increased recognition, awareness, and changes in diagnostic criteria for ASD (Elsabbagh 2012). ASD is more common in males, with a male‐to‐female ratio of 3:1 reported in a recently published meta‐analysis (Loomes 2017).

Aetiology

ASD is a neurodevelopmental disorder, with symptoms becoming noticeable in early childhood. Genetic problems, such as mutations, syndromes, and de novo copy number variations (a genetic alteration in which specific sections of genome are deleted or duplicated and can lead to phenotypic diversity among individuals), are thought to be the underlying cause in 10% to 20% of people with the condition (Abrahams 2008). The chance of people with ASD also having significant medical conditions ranges from 0% to 16.7%; tuberous sclerosis and fragile X syndrome are the conditions most commonly associated with ASD (Lyall 2017). Neuroimaging findings of abnormalities in the brain structure (dysfunctional activation and abnormal connectivity) of people with ASD reflect the clinical diversity of the condition (Ha 2015). Traffic‐related air pollutants, maternal immigrant status, advanced paternal age, low birth weight, prematurity, hyperbilirubinaemia, and pregnancy complications have been consistently reported as environmental risk factors for ASD (Ng 2017). Immune abnormalities, such as maternal infection during pregnancy, maternal autoimmune diseases, immune dysfunction and gastrointestinal dysfunction, are consistently reported in ASD (Matelski 2016). A recent review found that immune activation may be a cause of ASD or may just be an epiphenomenon (a secondary effect that occurs simultaneously with a disease but is not directly related to it) (Estes 2015).

ASD is comorbid with a number of other conditions; for example, 20% to 30% of children with ASD have seizure disorders (Tuchman 2010), 14% to 78% have attention deficit hyperactivity disorder (ADHD) (Gargaro 2011), and 11% to 84% have anxiety disorders (White 2009). A systematic review that focused on outcomes for people with ASD found that children with ASD whose early language acquisition was not considerably behind those of their peers at the age of five or six have better adult outcomes than those who showed a delay in language acquisition (Magiati 2014). That review also reported positive associations between these predictors and better adaptive, cognitive, communication, and social outcomes in adulthood (Magiati 2014).

A glutamate dysfunction has been implicated in comorbidities of ASD. For example, there is an increase in the downstream effects of glutamate signalling in people with tuberous sclerosis, of whom 20% to 40% have ASD (Rojas 2014). Blaylock 2009 theorised that the male predominance in ASD is attributable to foetal testosterone, which enhances glutamate receptor hyperactivity. Although glutamate is implicated in the pathophysiology of ASD, it is still not clear how glutamate dysfunction leads to the core symptoms of ASD (Rojas 2014). As such, glutamate, a major excitatory neurotransmitter, and its receptor, N‐methyl‐D‐aspartate (NMDA), have become targets in the treatment of ASD (Posey 2008).

Impact

Most children with ASD have reduced social functioning, cognitive ability and language skills, and increased mental health problems such as schizophrenia, obsessive compulsive disorder, depression and anxiety (Magiati 2014). Because of these problems, adolescents and young adults with ASD can face many challenges with independent living, friendship, education and job opportunities (Magiati 2014). Parents of children and adolescents with ASD have reported lower quality of life and higher levels of distress, anxiety and depression than parents of typically‐developing children (Padden 2017). In addition, parents may experience high levels of stress related to finding diagnostic and treatment services, the financial burden, and adapting family routines and daily activities (Schiltz 2018). Mothers of children with ASD have been found to exhibit higher levels of parenting stress than fathers (Pisula 2017). Parenting stress is often associated with poor mental health and emotional well‐being, which reduce involvement in social activities and worsen the negative social, emotional, academic and behavioral outcomes for both children with ASD and their parents (Schiltz 2018). For example, heightened parental stress and depression can worsen challenging behaviours in children with ASD, such as internalising (anxiety, sadness, reticence, fearfulness, and oversensitivity) and externalising behaviours (aggression towards others, hyperactivity, self‐harm, and conduct problems), because parents who are under stress may have difficulty in coping with the demands of parenting a child with ASD, and may not know how to respond best to challenging behaviours (Schiltz 2018).

The large social and financial burden of ASD affects the families of children with ASD, through lower levels of savings and reduced income, and is a potential burden for society as a whole (Ganz 2007). In the USA, the total annual societal costs of autism were estimated to be 35 billion USA dollars (USD) for the entire cohort of individuals with autism in 2007 (Ganz 2007). In the UK, the annual cost of supporting people with ASD was estimated to be 2.7 billion pounds sterling (GBP) for children and GBP 25 billion for adults; cost calculation adjusted to 2005/2006 price levels (Knapp 2009). In the USA, the annual cost of caring for people with ASD is expected to exceed USD 461 billion by 2025 (Leigh 2015).

Description of the intervention

Pharmacological interventions have been shown to be ineffective in treating the core features of ASD (NICE 2013), and drug treatment is currently considered a short‐ to medium‐term intervention for co‐existing psychiatric or neurodevelopmental conditions in individuals with ASD (SIGN 2016). D‐cycloserine has been widely researched in neuropsychiatric studies and findings suggest it may improve the negative symptoms of schizophrenia, such as affective flattening (lack of emotional reactions), loss of drive, anhedonia (loss of interest), and alogia (loss of fluency of thought) (Heresco‐Levy 2002). D‐cycloserine acts as a dose‐dependent agonist or antagonist to the NMDA receptor, and thus its dose needs to be optimised wisely (Goff 2012). Only low‐dose D‐cycloserine (50 mg/day) is associated with an improvement in the negative symptoms of people with schizophrenia. A higher dosage (more than 250 mg/day) does not show any greater improvement than 50 mg dosage and has more side effects (Goff 2012). Similarly, low‐dose D‐cycloserine given intermittently (once a week) up to 11 times appears to be effective in the extinction of fear in anxiety disorders (Rodrigues 2014).

Research in neuropsychiatric diseases suggests that D‐cycloserine may be of use in ASD, given the similarity between the negative symptoms of people with schizophrenia and the social impairment of people with ASD (Goff 1999), as well as the glutamate dysfunction found in both disorders (McDougle 2005). Specifically, an intermittent, once‐weekly dosage of D‐cycloserine persistently improves the negative symptoms of schizophrenia and memory consolidation, whereas the efficacy of D‐cycloserine is lowered in schizophrenia with regular and repeated use, due to tachyphylaxis (receptor down‐regulation) (Goff 2008).

For over 50 years, D‐cycloserine has been approved by the US Food and Drug Administration for use as a second‐line drug for the treatment of advanced tuberculosis, at a dose of up to 1 g (Schade 2016). The most common adverse effects of D‐cycloserine occur at doses higher than 500 mg (Schade 2016). With high‐dose D‐cycloserine, the reported rate of neurological adverse effects (commonly, seizure and neuropathy) is 1.1%, whereas the proportion of psychiatric adverse effects (notably psychosis, depression, risk of suicide) is higher, at 5.7% (Hwang 2013). Pyridoxine deficiency may also occur during treatment with D‐cycloserine, since D‐cycloserine is a pyridoxine antagonist and causes increased renal excretion of pyridoxine. D‐cycloserine is safe at low doses because, at low doses, the side effects are minimal or almost negligible (Schade 2016).

How the intervention might work

The specific mechanism by which D‐cycloserine may improve social and communication problems in ASD is not clear. D‐cycloserine is a partial agonist of the NMDA glutamate receptor with subunits known as NMDA Receptor 1 (NR1) and NMDA Receptor 2 (NR2) (A, B, C, D), which are activated by glycine and glutamate, respectively (Sheinin 2001). In addition, D‐cycloserine has partial agonistic action at NR1/NR2C receptors at low doses, and antagonistic action on NR1/NR2A and NR1/NR2B receptors at high doses (Danysz 1998). NMDA receptors are mainly found in the frontal cortex and hippocampus, and are responsible for sociability; diminished NMDA receptor–mediated signalling activity is associated with impaired sociability (Crino 2011). Blood glutamate levels are higher in people with ASD than in neurotypical people (Zheng 2016), and dysfunction of NMDA receptors at excitatory synapses has been found in people with ASD (Zhou 2013). As such, low‐dose D‐cycloserine, as an NMDA receptor agonist, has been used in the treatment of ASD for the improvement of social and communication skills.

Why it is important to do this review

ASD is one of the most common neurodevelopmental disorders, which can significantly reduce quality of life for both the person and his or her family. At present, there are no first‐line evidence‐based pharmacological treatments recommended for the core features of ASD; however, some medications are used for associated characteristics, for example behaviours of concern, hyperactivity (Sturman 2017), and anxiety or depression (Hurwitz 2012; Williams 2013).

There is some suggestion that D‐cycloserine may be an effective treatment for people with ASD; however, the effectiveness of the drug has not been thoroughly explored and there are currently no systematic reviews of the evidence. At present, any decisions about dosage and schedule of treatment rest on assumptions that what works in schizophrenia might work in ASD, because of some symptom similarities (Goff 2012). This review will, for the first time, collate all randomised controlled trial (RCT) evidence of the effect of D‐cycloserine compared to placebo for people with ASD. We will also explore the effectiveness of different dosages and drug administration frequencies.

Objectives

To assess the effectiveness and adverse effects of D‐cycloserine for children, adolescents, and adults with autistic spectrum disorder (ASD).

Methods

Criteria for considering studies for this review

Types of studies

Randomised controlled trials (RCTs), including both parallel‐group and cross‐over designs, of any duration.

Types of participants

Individuals with a diagnosis of ASD according to standard diagnostic classification systems, including: DSM‐III; DSM‐IV; DSM‐IV‐TR; DSM‐5; ICD‐10; ICD‐11; Autism Diagnostic Interview‐Revised (Lord 1994), and the Autism Diagnostic Observation Schedule (Lord 2000).

Types of interventions

D‐cycloserine for the treatment of ASD, irrespective of dose or duration, compared to placebo.

Types of outcome measures

Primary outcomes

  • Social communication and social interaction impairment, measured by validated instruments, such as the Social Responsiveness Scale (SRS; total raw score ranges from 0 to 195; higher scores indicate more significant social impairment; Constantino 2005), and other valid scales.

  • Restricted, repetitive, stereotyped patterns of behaviour and interests, assessed by valid scales, such as the Aberrant Behaviour Checklist (ABC) for stereotypies (Aman 1985), and other validated instruments.

  • Adverse events associated with D‐cycloserine, assessed by the number of non‐serious adverse events (headache, vomiting, cold, cough, restless, sadness, sedation, etc.) and serious adverse events (suicidal ideation etc.).

Secondary outcomes

  • Non‐core symptoms of ASD (e.g. anxiety, depression, parental stress level, sleep disorders, etc.), assessed using validated instruments, such as the Clinical Global Impressions ‐ Improvement (CGI‐I) scale, a 7‐point scale ranging from one (very much improved) to seven (very much worse) (Busner 2007), and other validated scales.

  • Tolerability of D‐cycloserine (adherence to treatment and follow‐up), assessed by the number of participants dropping out from studies, as well as the timing and reason for exclusion from studies, before study endpoints.

In keeping with time frames of other ASD intervention reviews (e.g. Williams 2013), we plan to assess outcomes at the following time points: short term (up to 3 months); medium term (3 to 12 months); and long term (over 12 months).

Search methods for identification of studies

Electronic searches

We will search the following electronic databases and trial registers.

  • Cochrane Central Register of Controlled trials (CENTRAL; current issue) in the Cochrane Library, which includes the Cochrane Developmental, Psychosocial and Learning Problems Specialised Register

  • MEDLINE Ovid (1946 onwards)

  • MEDLINE IN‐Process and Other Non‐Indexed Citations Ovid (current issue)

  • MEDLINE EPub ahead of Print Ovid (current issue)

  • Embase Ovid (1974 onwards)

  • PsycINFO Ovid (1806 onwards)

  • Conference Proceedings Citation Index – Science Web of Science (1970 onwards)

  • Conference Proceedings Citation Index – Social Sciences and Humanities Web of Science (1970 onwards)

  • Cochrane Database of Systematic Reviews (CDSR; current issue), part of the Cochrane Library

  • Database of Abstracts of Reviews of Effects (DARE; www.crd.york.ac.uk/CRDWeb)

  • Epistemonikos (www.epistemonikos.org)

  • ClinicalTrials.gov (www.ClinicalTrials.gov)

  • World Health Organization International Clinical Trials Registry Platform (WHO ICTRP; www.who.int/ictrp/en)

  • WorldCat (limited to dissertations and theses) (www.worldcat.org)

We will search all sources from inception to present, with no restrictions on language of publication. We will include studies reported as full text, those published as abstracts only, and unpublished data.

Searching other resources

We will check the bibliographies of all included studies for additional references to potentially relevant studies. We will contact experts working in the field, as well as the drug companies that make D‐cycloserine, to determine whether there are any ongoing or unpublished trials in this area. We will search relevant manufacturers’ websites for trial information, including the following specialised sources of adverse events reports: Australian Adverse Drug Reactions Bulletin (www.tga.gov.au/adr/aadrb.htm); Current Problems in Pharmacovigilance (www.mhra.gov.uk); European Medicines Evaluation Agency (www.emea.eu); and the US Food and Drug Administration (FDA) Medwatch (www.fda.gov/medwatch). Close to publication of the review, we will search MEDLINE and Embase to identify retractions, errors or corrections of included studies. We will also search RetractionWatch (retractionwatch.com).

Data collection and analysis

Selection of studies

Two review authors (SZA; HN) will independently review titles and abstracts obtained from the searches, discarding those that are clearly irrelevant. Next, they will retrieve the full‐text reports of all potentially relevant records (or those for which more information is needed to establish relevance), and assess them against the inclusion criteria (Criteria for considering studies for this review). They will resolve any disagreements by discussion or, if required, by consulting a third review author (HHS).

We will record the reasons for excluding ineligible studies that appear irrelevant at first glance in 'Characteristics of excluded studies' tables.

We will record the selection process in sufficient detail to complete a PRISMA flow diagram (Moher 2009).

Data extraction and management

Using a pre‐designed, piloted, standardised data collection form, two review authors (SZA, HN) will independently extract the following information from each included study.

  • Methods: study design; total duration of study; number of study centres and locations; study settings; withdrawals; date of study.

  • Participants: number; mean age; age range; gender; severity of condition; diagnostic criteria; inclusion and exclusion criteria.

  • Interventions: intervention; comparator; concomitant medications; excluded medications.

  • Outcomes: primary and secondary outcomes, with reported time points.

Two review authors (SZA; HN) will compare the extracted data for accuracy, resolving any discrepancies through discussion, or by involving a third review author (STM). One review author (QZ) will transfer data into Review Manager 5 (RevMan 5) and a second review author (HHS) will spot‐check the data entry for accuracy (Review Manager 2014).

Assessment of risk of bias in included studies

Following the guidance in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a), two review authors (SZA, HHS) will independently assess the risk of bias in each included study across each of the following seven domains: random sequence generation; allocation concealment; blinding of participants and personnel; blinding of outcome assessment*; incomplete outcome data; selective outcome reporting; and other bias. We will assign a rating of low, high or unclear (uncertain) risk of bias for each domain using the criteria set out in Appendix 2, and provide a quote from the study report together with an explanation for the judgment in the 'Risk of bias' table. We will resolve any disagreements by discussion or by involving a third review author (STM). When we consider the risk of bias to be unclear due to insufficient information, we will contact the study authors for additional detail and record this in the 'Risk of bias' table.

*We will consider blinding of outcome assessment separately for different outcomes, where necessary (e.g. for unblinded outcome assessment, risk of bias for all‐cause mortality may be very different to that for participant‐reported stress levels).

Measures of treatment effect

We will perform a meta‐analysis if we identify two or more studies measuring the same outcome, with similar interventions and participants, and addressing similar underlying clinical questions.

Dichotomous data

We will analyse dichotomous outcome data as risk ratios (RR) and present these with 95% confidence intervals (CI). In addition, we will calculate the number needed to treat for an additional beneficial or harmful outcome (NNTB or NNTH); that is, the number of participants needed to obtain a beneficial or harmful outcome with the intervention, using the pooled RR.

Continuous data

We will report continuous data as mean differences (MD) when studies assess the same outcome using the same scale, and standardised mean differences (SMD) when studies use different scales to measure the same outcome. We will present the MD and SMD with 95% confidence intervals. For SMD, we will interpret the effect sizes using Cohen's 'rule of thumb', i.e. 0.2 represents a small effect, 0.5 a moderate effect, and 0.8 a large effect (Cohen 1988).

Unit of analysis issues

We will analyse the number of participants as the unit of analysis, managing potential unit of analysis issues as follows.

Cross‐over trials

For cross‐over trials, we believe that there will be a carry‐over effect of D‐cycloserine that will outlast any washout period, thereby making it impossible to gain 'clean' results unaffected by the first period (Attari 2014). Hence, we will only include data from the first period in a meta‐analysis.

Studies with different doses of D‐cycloserine

If we identify studies assessing different doses of D‐cycloserine in one trial, we will combine all dose groups and compare them with the results of the placebo group, making single pair‐wise comparisons; for dichotomous outcomes, we will sum the sample sizes and the numbers of people with events across groups, whereas for continuous outcomes, we will combine the means and standard deviations using the formula described in Section 7.7.3.8 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011b).

If primary studies report the results as a single value for the placebo arm (e.g. in a trial of D‐cycloserine with low‐dose, high‐dose and placebo arms, where the outcomes for the placebo group are reported as one whole value), we will split the data proportionately according to the number of dosage groups, to avoid double counting the participants. For dichotomous outcomes, we will divide both the number of participants and the number of events by the number of dosage groups in the study. For continuous outcomes, we will divide the total number of participants only, leaving the means and standard deviations unchanged.

Dealing with missing data

We will contact the study authors and request them to supply any missing numerical outcome data, or to provide an explanation about why they are missing and not available in the study report. If data are made available to us, we will include them in the analyses. If not, we will report our attempts to obtain the missing outcome data and its potential impact on the findings of the review in the Discussion section. We will not impute the missing data, and we will only analyse the available data, to avoid any potential artificial inflation of the precision of the effect estimate (Higgins 2011b).

Assessment of heterogeneity

We will assess clinical, methodological, and statistical heterogeneity. For clinical heterogeneity, we will compare the distribution of participant characteristics between studies (e.g. age, gender), whereas for methodological heterogeneity, we will compare trial characteristics such as randomisation, allocation concealment, blinding, and loss to follow‐up. For statistical heterogeneity, we will visually inspect the horizontal lines representing each trial on the forest plot for overlapping CI. We will also use the Chi2 test for homogeneity (interpreting a P value less than 0.10 as heterogeneity) and the I2 statistic to quantify the degree of heterogeneity. We will interpret the I2 values using the following thresholds:

  • 0% to 40%: might not be important;

  • 30% to 60%: moderate heterogeneity;

  • 50% to 90%: substantial heterogeneity; and

  • 75%to 100%: considerable heterogeneity.

As these thresholds can be misleading, when considering the importance of I2, we will take into account the magnitude and direction of effects, and strength of evidence for heterogeneity based on the P value from the Chi2 test (Deeks 2011).

When the I2 value exceeds 50%, we will use a random‐effects model for meta‐analysis. We will also report Tau2, a measure of between‐study variance, when reporting results from the random‐effects model (Data synthesis).

If we consider that the studies are too clinically heterogeneous, we will not conduct a primary meta‐analysis and will provide a narrative synthesis of the results instead (Data synthesis).

We will explore possible sources of heterogeneity by conducting prespecified subgroup analyses (Subgroup analysis and investigation of heterogeneity).

Assessment of reporting biases

We will try to obtain the protocols of each included study, to compare the outcomes reported in the protocol with those reported in the published paper. If we are able to pool more than 10 studies in a meta‐analysis, we will draw a funnel plot and examine it for any asymmetry, which may be due to the following reasons stated in Section 10.4.2 of the Cochrane Handbook for Systematic Reviews of Interventions:

  • publication bias;

  • selective outcome reporting;

  • poor methodological quality causing spuriously inflated results in smaller studies;

  • true heterogeneity, where the size of the effect differs according to the size of the study; and

  • sampling variation leading to an association between the intervention effect and its standard error (Sterne 2011).

We will make explicit judgements about whether studies are at high risk of bias, according to the criteria given in the Cochrane Handbook for Systematic Reviews of Interventions (Sterne 2011). While funnel plots may be useful in investigating reporting biases, there is some concern that tests for funnel plot asymmetry (e.g. Egger's regression test; Egger 1997) have limited power to detect small‐study effects, particularly when there are fewer than 10 studies or where all studies are of similar sample size. We will consult with a statistician before implementing such tests (Sterne 2011).

Data synthesis

If we are able to identify two or more studies with sufficient clinical and methodological homogeneity, we will pool the data in a meta‐analysis using a fixed‐effect model in RevMan 5 (Review Manager 2014).

'Summary of findings' table

We will create a 'Summary of findings' table using GRADEpro GDT software (GRADEpro GDT). We will include in this table all primary and secondary outcomes, assessed at three to six months' follow‐up:

  • social communication and social interaction impairment;

  • restricted, repetitive, stereotyped patterns of behaviours and interests;

  • adverse events;

  • non‐core symptoms of ASD; and

  • tolerability of D‐cycloserine.

Following the recommendations in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a; Schünemann 2011), two review authors (SZA; HN) will independently evaluate the quality of the evidence for each outcome as high, moderate, low or very low, according to the five GRADE criteria: study limitations, consistency of effect, imprecision, indirectness, and publication bias. We will report these ratings in the table, along with our decisions for downgrading the quality of the evidence, if relevant. We will also include any comments to facilitate better understanding of our review by readers.

Subgroup analysis and investigation of heterogeneity

We will carry out subgroup analyses if a sufficient number of trials report the following factors.

  • Dose of D‐cycloserine: low dose (< 50 mg) versus high dose (> 50 mg)

  • Dosing interval of D‐cycloserine: once‐weekly dose versus daily dose

  • Age range: early childhood (2 to 5 years old); middle childhood (6 to 11 years old); adolescence (12 to 21 years old); and adults (more than 21 years old) (Williams 2012)

  • Intellectual disability: participants with intellectual disability versus those without

  • Comorbity: participants with comorbidity (e.g. anxiety, ADHD, other mental health disorders) versus those without

Sensitivity analysis

We will perform sensitivity analyses by repeating the meta‐analyses after excluding trials with:

  • unclear or high risk of performance and detection bias due to lack of appropriate blinding; and

  • trials where missing data were imputed by the authors.

We will conduct additional sensitivity analyses if we identify other issues that might impede our confidence in the estimated pooled effect sizes such as source of recruitment (population or clinical), non‐compliance or protocol violation, different definitions for the outcomes, outliers or baseline imbalance across the intervention arms in primary studies.

If we find substantial differences in effect estimates in any of the sensitivity analyses, we will present the results separately; that is, we will not present the pooled data.

Acknowledgements

The protocol was developed under the support and guidance of the Cochrane Developmental, Psychosocial and Learning Problems (CDPLP) Editorial Team. We would like to acknowledge Margaret Anderson, Information Specialist of CDPLP, for her input in writing the search strategy, and Joanne Duffield for advice and guidance. In addition, we would like to thank the following reviews for their helpful comments on an earlier version of this protocol: Dr Clare S Allely, Reader in Forensic Psychology, School of Health Sciences, University of Salford, Manchester, UK and affiliate member of the Gillberg Neuropsychiatry Centre, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Edward S Brodkin MD, Associate Professor of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA; Dimitris Mavridis, Department of Primary Education, University of Ioannina, Ioannina, Greece; and Danial Sayyad, Iran.

Appendices

Appendix 1. MEDLINE search strategy

1 exp child development disorders, pervasive/ 2 pervasive development$ disorder$.tw,kf. 3 (pervasive adj3 child$).tw,kf. 4 (PDD or PDDs or PDD‐NOS or ASD or ASDs).tw,kf. 5 autis$.tw,kf. 6 asperger$.tw,kf. 7 childhood schizophrenia.tw,kf. 8 Rett$.tw,kf. 9 or/1‐8 10 Cycloserine/ 11 D‐cycloserin$.mp. 12 cycloserin$.mp. 13 or/10‐12 14 9 and 13 15 exp animals/ not humans/ 16 14 not 15

Appendix 2. 'Risk of bias' assessment

We will judge the risk of bias in the included studies according to the criteria described below (see Higgins 2011a).

Random sequence generation (checking for possible selection bias)

We will judge the risk of selection bias related to random sequence generation as follows.

  • Low: investigators describe a random component in the sequence generation process such as referring to a random number table, using a computer random number generator, coin tossing, shuffling cards or envelopes, throwing dice, drawing of lots, or minimisation.

  • High: investigators describe a non‐random component in the sequence generation process, for example, sequence generated by odd date or even date of birth, sequence generated by some rule based on date (or day) of admission, or sequence generated by some rule based on hospital or clinic record number. Other non‐random approaches that happen much less frequently usually involve judgement or some method of non‐random categorisation of participants, such as allocation by judgement of the clinician, allocation by preference of the participant, allocation based on the results of a laboratory test or a series of tests, and allocation by availability of the intervention.

  • Unclear: information about the sequence generation process is not clearly stated or insufficient to permit judgement of low or high risk of bias.

Allocation concealment (checking for possible selection bias)

We will judge the risk of selection bias related to allocation concealment as follows.

  • Low: participants and investigators enrolling participants could not foresee assignment because one of the following, or an equivalent method, was used to conceal allocation: central allocation (including telephone, web‐based, and pharmacy‐controlled randomisation); sequentially‐numbered drug containers of identical appearance; sequentially‐numbered, opaque, or sealed envelopes.

  • High: participants or investigators enrolling participants could possibly foresee assignments and thus introduce selection bias; for example, allocation based on: using an open random allocation schedule (e.g. a list of random numbers); using assignment envelopes without appropriate safeguards (e.g. if envelopes were unsealed or non‐opaque or not sequentially numbered); alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure.

  • Unclear: information about allocation concealment is not clearly stated or is insufficient to permit judgement of low or high risk of bias.

Blinding of participants and personnel (checking for possible performance bias)

We will judge the risk of performance bias as follows.

  • Low: blinding of participants and key study personnel is ensured and it is unlikely that the blinding could have been broken; no blinding or incomplete blinding, but the outcome is not likely to be influenced by lack of blinding.

  • High: no blinding or incomplete blinding and the outcome is likely to be influenced by lack of blinding; blinding of key study participants and personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding.

  • Unclear: study did not address this outcome; or there is insufficient information to permit judgement of low or high risk of bias.

Blinding of outcome assessors (checking for possible detection bias)

We will judge the risk of detection bias as follows.

  • Low: no blinding of outcome assessment, but the outcome measurement is not likely to be influenced by lack of blinding; blinding of outcome assessment ensured, and unlikely that the blinding could have been broken.

  • High: no blinding of outcome assessment and the outcome measurement is likely to be influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement is likely to be influenced by lack of blinding.

  • Unclear: study did not address this outcome; or there is insufficient information to permit judgement of low or high risk of bias.

Incomplete outcome data (checking for possible attrition bias)

We will judge the risk of attrition bias as follows.

  • Low: no missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to be introducing bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, proportion of missing outcomes compared with observed event risk is not enough to have a clinically relevant impact on intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardised difference in means) among missing outcomes not enough to have a clinically relevant impact on observed effect size; missing data have been imputed using appropriate methods.

  • High: reasons for missing outcome data likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in intervention effect estimate; for continuous outcome data, plausible effect size (difference in means or standardised difference in means) among missing outcomes enough to induce clinically relevant bias in observed effect size; 'as‐treated' analysis done with substantial departure of the intervention received from that assigned at randomisation; potentially inappropriate application of simple imputation.

  • Unclear: study did not address this outcome; or there is insufficient reporting of attrition/exclusions to permit a judgement of low or high risk of bias (e.g. number randomised not stated, no reasons for missing data provided).

Selective outcome reporting (checking for possible reporting bias)

We will judge the risk of reporting bias as follows.

  • Low: study protocol is available and all of the study’s pre‐specified (primary and secondary) outcomes that are of interest to the review are reported in the pre‐specified way; or the study protocol is not available, but it is clear that the published reports include all expected outcomes, including those that were pre‐specified.

  • High: not all of the study’s pre‐specified primary outcomes are reported; one or more primary outcomes are reported using measurements, analysis methods, or subsets of the data (e.g. subscales) that were not pre‐specified; one or more reported primary outcomes were not pre‐specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect); one or more outcomes of interest in the review is reported incompletely so that it cannot be entered in a meta‐analysis; or the study report fails to include results for a key outcome that was expected to be reported for such a study.

  • Unclear: insufficient information to permit judgement of low or high risk of bias.

Other sources of bias (checking for other potential sources of bias)

We will judge the risk of other bias as follows.

  • Low: study appears to be free of other sources of bias.

  • High: at least one important risk of bias exists (for example, the study has a potential source of bias related to the specific study design used, or has been claimed to have been fraudulent, or has had some other problem).

  • Unclear: potential risk of bias, but there is either insufficient information to assess whether an important risk of bias exists or insufficient rationale or evidence that an identified problem will introduce bias.

Contributions of authors

Swe Zin Aye (SZA) generated the idea, wrote the protocol, and has overall responsibility for this review.

Han Ni contributed to the writing of the protocol and supervised the overall production of the protocol.

Htwe Htwe Sein provided feedback on the protocol.

San Thidar Mon provided feedback on the protocol.

Qishi Zheng provided feedback on the protocol.

Yoko Kin Yoke Wong provided feedback on the protocol.

Sources of support

Internal sources

  • QUEST International University Perak, Malaysia.

    Permitted Swe Zin Aye and Htwe Htwe Sein to work on this protocol during office hours

  • SEGi University, Malaysia.

    Allowed Han Ni to work on this review during office hours

External sources

  • None, Other.

Declarations of interest

Swe Zin Aye ‐ none known.

Han Ni ‐ none known.

Htwe Htwe Sein ‐ none known.

San Thidar Mon ‐ none known.

Qishi Zheng ‐ none known.

Yoko Kin Yoke Wong ‐ none known.

New

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