72 – A Data-Driven Approach to Reconstructing Disease Trajectories in SYNGAP1-Related Disorders

Jillian McKee, MD, PhD


Jillian McKee, MD, PhD, is an epilepsy and neurogenetics fellow in the Division of Neurology at the Children’s Hospital of Philadelphia. She attended McGill University for her undergraduate studies and completed medical school and earned a PhD in computational neuroscience at the University of Chicago, employing machine learning techniques to understand the neural underpinnings of visual learning and decision making. Dr. McKee then moved to Philadelphia, where she completed pediatrics and neurology residencies as well as fellowships in epilepsy and neurogenetics at the Children’s Hospital of Philadelphia. Dr. McKee’s research focuses on using multi-omics and electronic medical data to understand disease trajectories in genetic epilepsies, including SYNGAP1-related disorders, and employing machine learning techniques for outcome prediction and the development of targeted therapeutics.


0:06hello everybody and welcome to today’s webinar  my name is Olga Bothe and I’m a parent and a part   of the team here at syngap research fund our  presentation today is a data-driven approach  

0:17to reconstructing disease trajectories and sing  get one Related Disorders I have the pleasure to  

0:23introduce today’s speaker Dr Jillian McKee Dr  McKee is an epilepsy and neurogenetics fellow  

0:29in the division of Neurology at the Children’s  Hospital of Philadelphia she attended McGill  

0:34University for her undergraduate studies and  completed medical school and earned a PhD in  

0:39computational neuroscience at the University  of Chicago employee machine learning techniques  

0:45to understand the neural underpinnings  of visual learning and decision making  

0:50Dr McKee then moved to Philadelphia where she  completed Pediatrics and Urology residencies as  

0:56well as fellowships in epilepsy and urogenetics  at the Children’s Hospital of Philadelphia  

1:02her research focuses on using multi-omics and  electronic medical data to understand disease  

1:08trajectories in genetic epilepsies including  SYNGAP1 Related Disorders and employing machine   learning techniques for outcome prediction  and the development of targeted Therapeutics  

1:19a recorded version of this webinar will  be available on the SRF website under   webinars on the family menu and by the end of the  presentation you will have the opportunity to have  

1:30your questions answered we’d love to hear from you  so please write those questions in the Q&A below  

1:37for those of you just joining us again our speaker  today is Dr Jillian McKee her presentation is a  

1:44data-driven approach to reconstructing disease  trajectories in SYNGAP1 Related Disorders  

1:50it’s now my pleasure to  turn things over to Dr McKee   thank you so much Olga and thank you Mike  and the rest of the sync app research fund  

1:59for inviting me here to speak today I’m really  excited to share what we’ve been working on at   chop using large-scale data to help understand the  disease trajectories in SYNGAP1 Related Disorders

2:13so just to start off I’m just going to go  through a brief outline of my talk today  

2:18so first we’re just going to review briefly the  current state of Syngap research including what’s  

2:24known about both the clinical disease trajectories  and the variant Spectrum next I’m going to provide  

2:31a very brief overview of phenotypic science to  orient you to the rest of the analyzes I will be  

2:36sharing later in the talk and then finally  what we’re going to spend most of our time   on today is talking about how we can leverage  real world data from multiple different sources  

2:47um different sample sizes  different levels of granularity   um to help delineate disease  trajectories in SYNGAP1 Related Disorders

3:00so to start off let’s review what we all know now

3:07so as most of you are already aware the SYNGAP1  gene was first discovered actually quite a long  

3:12time ago in 2000 a long time ago with genetics  land I guess in 2009 and it’s among the most  

3:19common genetic Developmental and epileptic  encephalopathies it tends to be characterized  

3:24most significantly by developmental delay  and intellectual disability autism and then  

3:30generalized seizures and it’s actually one  of the few monogenic causes of generalized  

3:36epilepsy including a specific epilepsy syndrome  called myophonic astatic epilepsy also known  

3:42as Mae or Doose syndrome so despite significant  research interest one problem has been trying to  

3:51identify genotype phenotype correlations these  still remain elusive in Syngap and we’ll talk   a little bit about why that could be so I’m not  intending this to be a comprehensive literature  

4:03review by any means um but rather just want to  broadly um review a little bit what’s out there  

4:09in the existing scientific literature um so this  2019 study by Vlascamp and colleagues describe  

4:16the phenotypic Spectrum in 57 individuals with  SYNGAP1 Related Disorders they looked in depth  

4:23at the epilepsy phenotypes in particular showing  that generalized seizures especially epilepsy  

4:28with eyelid myoclonia and epilepsy with  myoclonic atonic seizures were most common  

4:34and they also did a really really nice job  looking at co-morbidities showing not only  

4:40the most universally recognized intellectual  disability but also really highlighting some   of the behavioral issues that I know many  of individuals with Syngap struggle with

4:55um so in addition to generalize description  of clinical features we naturally want to  

5:03know how the specific change the specific  genetic change in an individual’s Syngap Gene  

5:09um affects their disease course and that has  been difficult in Syngap and this 2016 study  

5:16try to address this question and found that  in a small cohort of 17 individuals those  

5:22with variants in exons four and five had  seizures that tended to respond better to  

5:27seizure medications while those with  variants and exons 8 to 15 tended to   have more drug resistant seizures and while  this is a promising start but unfortunately  

5:38hasn’t been that much further data to support  strong genotype phenotype correlations to date

5:51um so as you can see here in this figure from a  recent blog post by one of our genetic counselors   Natalie changes in the syngap gene are very broad  they range from single amino acid substitutions to  

6:03what we call protein truncating variants that  just stop the protein early to whole exome on  

6:10whole Exxon multi-axon and multi-gene deletions  and while we know that the overall mechanism of  

6:16disease is loss of function or what we call  haploinsufficiency meaning one copy of the   gene is not working teasing apart the specific  genotype phenotype correlations can be difficult

6:29so given that background here are the specific  research goals that I want to discuss with you  

6:36guys today so our first goal is to use  phenotypic science and data integration  

6:41from multiple different sources to help us  better understand disease histories over time  

6:48we want to identify features that are unique to  SYNGAP1 compared to other Pediatric epilepsies  

6:54and other genetic epilepsies we want to try to  explore those genotype phenotype correlations  

7:00and then we want to get a sense of caregiver  perspectives through disease concept models  

7:06and then finally going to talk a little bit  about natural history studies and how we can   incorporate rigorous questionnaires clinical  skills physical exams and even quantitative  

7:14EEG to get even more fine detailed data about  how SYGAP1 clinical features vary over time  

7:25foreign so first let’s talk a  little bit about phenotypic science  

7:31if my slide will progress there we go so my  research Mentor Ingo Helbig loves to display  

7:38this slide towards the beginning of his talks  just to convey the immensity of data we’re able  

7:43to access through electronic medical records so  this plot shows over 1 million neurology notes for  

7:51over a hundred thousand individuals capturing over  120,000 patient years so the scale of this data is  

7:58truly incredible but because it’s so large and it  necessitates rigorous methods for transforming and  

8:05analyzing the data um so this is where we start  to get into data harmonization and ontologies  

8:13there are many different approaches to phenotyping  and I’m just going to say right off the bat that  

8:19none of these are perfect it’s always a constant  trade-off between sample size and data granularity  

8:26we obviously want to maximize both and be up here  with the largest possible sample size with the  

8:31most fine detailed information we can possibly  obtain but those those improaches just aren’t  

8:38feasible at least not currently and then methods  with very small sample sizes and not very detailed  

8:45information are not useful so we’re left with  approaches that fall along this diagonal line here  

8:53thank you so large-scale phenotyping such as  in this neighborhood analysis uses very large  

9:00sample sizes but lacks the granular detail  and then in contrast you have natural history  

9:06studies which record data about individuals  at the highest level of detail but as such  

9:12can only handle small sample sizes so it’s  a constant trade-off so which method is best

9:20and I’m hoping to convince you today that by  using real world data that spans the Spectrum  

9:26we can get the best of both worlds to some  degree so let’s pivot and look at our sources  

9:32of real world data and how they compare in terms  of both the sample size and the level of detail

9:40so today I’m going to show you  analysis using data from three   different sources that’s kind of run the  gamut Span in the spanning the Spectrum  

9:50um so first we’re going to talk a bit about  some insurance claims data that we’ve been  

9:55analyzing in collaboration with the team at  Ambit they have a very large sample size but  

10:01because it’s based on insurance claims it  lacks the same level of detail in the middle  

10:06there is Ciitizen consisting of curated medical  record data with a moderately large sample size  

10:13and level of detail and then finally there’s  our internal chop cohort where we have excess  

10:19access to very very detailed medical records  but our numbers are comparatively very small

10:29so we’ve been collaborating with Ambit which is  a company whose goal is to provide solutions to  

10:35help identify patients with rare disease and to  help facilitate clinical trials and disseminate  

10:40approved Therapies so they do this by identifying  patients from large insurance claims database  

10:47and as they use a large claims  database for their analyzes they   have access to de-identified Patient  data on 582 individuals with syngap

10:58so I just want to briefly credit some of our  collaborators at Ambit Rob Akash and Chen  

11:05they’ve been helping us put together some  of this thing got one analyzes that I’ll be   sharing today so just want to give them a brief  shout out and then before moving on to our next  

11:14data source I just wanted to take one second to  put in a plug for ICD-10 codes for rare diseases  

11:20um we’re only able to do these Insurance  claim analyzes because Syngap has an   ICD-10 code some of some of our other rare  neurodevelopmental disorders including STXBP1,  

11:31SCN2A, SCN8A they don’t yet have ICD-10 codes  so we’re very fortunate working on Syngap to  

11:39have the ability to use this claims data set  due to the advocacy of the Syngap community

11:49so our next source of real world  data is likely familiar to most   certain viewer many of you on  this call as Mike Anderson Gap  

11:57research fund have been one of their more  vocal cheerleaders and that’s Ciitizen  

12:03as you may know they aggregate data from medical  records and provide it to researchers Physicians  

12:08pharmaceutical companies with the goal of  advancing targeted therapies for rare disease and  

12:15here I’m just showing you some of their Partners  including of course the Syngap Research Fund

12:22and then finally at CHOP we follow a  comparatively small cohort of SYNGAP1  

12:28patients through our epilepsy neurogenetics  initiative on this cohort we have access  

12:33to detailed clinical histories medication  prescriptions lab work Imaging EEG results   all the information that’s gathered through  our routine clinical care of these patients

12:46so by harmonizing these three sources of data   we’re able to generate a more complete view of  the clinical histories of our patients over time

13:04so just a quick reminder about the three cohorts  and where they fit in so Ambit this is the claims  

13:09data set using the ICD-10 code and they have  a total of 582 patients with syngap most of  

13:16the analyzes I’m going to show you are focusing  on a smaller subset of these just to focus more   on Pediatrics um than citizen um the the data set  that’s included in the analyzes I’m going to show  

13:29you has 150 patients but they now I’ve heard have  over 200 enrolled and this is curated EMR data  

13:38um and then finally our local CHOP cohort where we  have about 30 patients with Syngap where we have  

13:44information in our local electronic medical record   some of them obviously are followed more  longitudinally and we have more detailed data  

13:51than others um but it tends to be more extensive  detailed data than the other two resources

14:01so here is an overall characteristic  characterization of our combined CHOP  

14:07and Ciitizen cohorts um the figure on the left  here is showing time stamped data points for each  

14:14individual plotted against the individual’s  age at the time that data was acquired  

14:21um so we definitely have more data on younger  patients as you can see the points tend to cluster  

14:26on this side of the plot but we do have some  individuals with data through their teenage years  

14:32and then here on the right you can just see a  summary table showing the clinical features of  

14:38this combined group um so as I just mentioned they  tend to be young with a median age of 6.7 years  

14:46and then hearkening back to the variant Spectrum  most individuals tend to have protein truncating  

14:51variants so those are the variants that either  introduce a stop codon too early or a frame shift   and then a stop and it cuts the protein short and  there’s it’s just degraded and there’s no protein  

15:02um well only 13 percent have misense variants  

15:08in this cohort um and the epilepsy phenotype  instant Gap is quite Unique Individuals have  

15:15generalized seizures with 27 percent having  absence or those staring seizures 23 percent  

15:22heavy myoclonic are those quick body jerk  seizures and 30 having atonic or drop seizures  

15:29um and many children as I mentioned before with  syngap are diagnosed with that specific epilepsy   syndrome called myoclonicatonic epilepsy or  Dosa syndrome um and there’s actually only  

15:39two other monogenic epilepsies associated with  this syndrome and those are slc6a1 and netsmith

15:48um so the most commonly reported clinical feature  is developmental delay or intellectual disability  

15:54and that’s present in 97 of this cohort other  very common features include hypotonia and autism  

16:03and finally something that is somewhat unique  to syngap is behavioral concerns are very very  

16:09common they’re reported in 93 percent  of individuals and especially aggression  

16:15attentional concerns including ADHD and then  problems with sleep are also very very common

16:27so now what can we do if we look at the broader  population that’s captured by claims data  

16:34so since the introduction of the ICD-10 code  in 2021 Ambit has been able to identify 582  

16:42individuals with syngap and for mostly analyzes  I’m going to show here we restricted those to  

16:48under age 20 so that includes 236 individuals  um we did this to facilitate the comparisons  

16:56across our data resources because most of  our other sources only have younger patients  

17:02um but we don’t want to ignore the older  population I think it’s actually really great   that Ambit has so much information on adults  with syngab and this is definitely going to  

17:11be an interesting um group to characterize more  thoroughly because we just we don’t know we don’t  

17:18have a lot of information on what do people with  syngap look like when they are 30 or 60 um so I do  

17:25think I’m not don’t want to ignore this population  we’re just limiting us to us to under 20 for now

17:33um so here on the left you can see the monthly  number of new patients with the ICD-10 code based  

17:40diagnosis um and then the cumulative number is  shown here in overlay and then on the right I’m  

17:49showing you a similar plot to the one I showed you  for the chop and citizen data with each horizontal  

17:54line representing one individual with start and  stop points corresponding to the ages for which   they have data in the claims database and you’ll  notice that most the width of most of these bars  

18:04is a maximum of six years and that’s due to this  database only having six years of data um but so  

18:12while we’re all ready with this data as I’m going  to show you able to learn new insights um this is  

18:17a resource that’s only going to get more and  more useful over time now that we have the   ICD-10 code more patients are going to be um code  their prescriptions their clinical features are  

18:29going to be coded with that diagnosis and we’ll  be able to pull all of that data in the future

18:37so slipping back to Citizen for a second one  really great aspect of the citizen data set  

18:44is the developmental milestones so here you can  see for all individuals that attain a given skill  

18:50shown here um the cumulative percent of people  who have achieved that skill by a given age  

18:58um so and the dash vertical lines here represent  the age at which 90 of people who ultimately  

19:04attain that skill have have reached that  point um so for example 90 of patients who  

19:13ultimately walk unassisted so this dark purple  line have achieved that skill by age three

19:24so once we’ve extracted and harmonized all  this data we can start to ask questions  

19:30such as how does the prevalence of certain  clinical features change across the lifespan  

19:35so here I’m showing you the presence in red and  the absence in blue of four features that are  

19:41commonly associated with singap and how those  change over time so compared to just a static  

19:48proportion of individuals adding the dimension  of time really gives us a better sense of what  

19:54to expect over the course of a child’s lifetime  for example you can see that well most individuals  

20:01have seizures during their lifetime actually  very few people have seizures in the first  

20:07two years of life you can see that here these  bars are all blue for the first few time bins  

20:15um and then in contrast hypotonia is very  very common early in life but becomes less  

20:21noticeable or just less documented um  over the course of someone’s lifespan

20:32um so in addition to looking broadly at  just the presence or absence of seizures  

20:37we can grade the severity of seizures over  time in monthly time bins to get a picture  

20:43of the dynamic nature of seizures in syngap so  here you can see the proportion of individuals  

20:50with different seizure severities coded  by color over the first five years of life  

20:57so we break this down into six different  categories seizure-free monthly seizures weekly  

21:03seizures daily seizures several seizures per day  and many per day um and then for each monthly  

21:10time bin we show the proportion of patients which  falls into each of those categories and then it’s  

21:15kind of probably kind of hard to see for you guys  on the computer screens but between um each bar  

21:22there’s little connectors and blue connectors  mean that someone sees your frequency went  

21:27down so it improved and yellow connectors mean  their seizure frequency went up so it got worse  

21:33um and one unique thing about  syngap compared to many other  

21:39um Developmental and epileptic encephalopathies  is that seizures tend to start a little later  

21:44as you can see here usually in  the second or third year of life

21:51and here you can see a direct comparison of  syngap to some of our other common genetic   epilepsies and it’s really striking how  different the like seizure trajectory is  

22:02in syngap compared to these other causes like  stxp1 scn2a scn8a all have really significant  

22:10proportions of infantile onset seizures  whereas in syngap it’s that’s really absent

22:20so so far we looked at milestones and how  phenotypes change over time but what about  

22:26medications using using our prescription data  extracted from our local medical record and then  

22:32combining this with what’s available on medication  data within citizen we can look at the medication  

22:39use over time for both anti-seizure medications  and then medications that are used for Behavioral  

22:45purposes such as sleep attention or aggression so  for seizures what you see here kind of confirms  

22:54my clinical intuition there’s a lot of Keppra you  can see here levatoracetam there’s a good amount  

23:00of valproate or Depakote being used and amphi or  clobazam but it’s a little bit all over the place  

23:09um and then oh yeah followed a little later by  more Lamotrigine um and then for Behavior it  

23:15seems to be dominated by sleep aids early on a  lot of melatonin and then you only start to see  

23:20antipsychotics like Risperdal appearing later  once these behaviors become harder to control

23:31so when you ask the same question using claims  data the answer looks a little different  

23:37um I’m showing you right here the same this  is the same chop citizen figure on the left   and then an equivalent one um generated  by claims David data by Ambit on the right  

23:50and while there’s still a lot of Depakote here you  see a lot of on fee up here that’s a clb is on fee  

23:57and Lamictal here in the middle LTG but there’s  less Keppra and then you notice um zenissamide  

24:05and Topamax here um which are two similar  medications that were virtually absent in our  

24:12data there’s like tiny little slivers of them um  become much more prominent and the exact reasons  

24:19for this are still unclear we’re going to have to  look into it a little more but it may have to do   with differing prescribers and just the overall  differences in the populations we’re assessing

24:30foreign so one unique thing about the claims data  is it’s relatively easy to identify comparator  

24:39groups which would be very difficult for us with  our local EMR to have to manually go through and  

24:45like reconstruct seizure frequencies for hundreds  of children with epilepsy but with the claims data  

24:51it’s relatively easy for Ambit to go and pull  a comparator group so here I’m showing you the  

24:57same syngap medication landscape on the left from  Ambit um but compared with one that the ambiting  

25:03created for all patients with a diagnosis of Linux  Gusto syndrome and this one’s quite remarkable  

25:10because it incorporates data from over 17 000  individuals illustrating the power of claims data  

25:18um but it really also highlights the unique  um patterns of medication use within syngap

25:29so now that we have detailed seizure  reconstructions over time and we’re able to  

25:34get a sense of medication prescription information  over time we can combine these two into a  

25:42comparative effectiveness framework to assess  which medications may be the most helpful which  

25:48is ultimately what people want to know right which  medication should we prescribed for this child  

25:54um so much of this work has been done by Julie  who’s a data scientist in our lab so at the  

25:59top here I’m showing you a schematic example of  seizure frequency over time in just one individual  

26:06and then on the bottom are the prescription dates  and periods of use for different medications  

26:13so highlighted in green are periods of seizure  reduction so times when the seizure frequency  

26:19drops so here and here and then highlighted in  blue are periods of maintained seizure freedom

26:30so using this data we can compute odds ratios  of the effectiveness of either reducing seizure  

26:38frequency using the medications that they’re  on during those green periods and maintaining  

26:44seizure Freedom by using the data from the periods  of those blue periods where they’re seizure free  

26:50um and I’m showing you both of these in B and  C so B are the odds ratios for common seizure  

26:56medications for reducing seizure frequency  and in C is for maintaining seizure freedom  

27:03and what we see is that Depakote seems to be the  most effective for both reducing seizure frequency  

27:09and maintaining seizure freedom um while Lamictal  is effective at maintaining seizure freedom and  

27:16it’s well this will improve as we have bigger  sample sizes but most of these other medications  

27:21um their confidence interval passes crosses  one so they’re just they haven’t reached   significance but there are other medications  on this list that may be promising as well

27:34so now taking a step back and going back towards  phenotyping with claims data we can take advantage  

27:40of the fact that um the database doesn’t just  include patients with syngap or genetic epilepsies  

27:46to help us further Define the characteristics  unique to singap so here we’re comparing  

27:54um the frequencies of different clinical  features within individuals with syngap  

28:00here on the y-axis axis to all individuals with a  G40 plus ICD-10 code diagnosis so what that means  

28:09is that’s just a broad group incorporating  all different seizure and epilepsy codes  

28:14so here the red dots represent significant  associations so as you can see there’s  

28:20actually quite there’s very many um and we’ve  just labeled a few um and they’re the ones that  

28:27we would expect like intellectual disability  autism behavioral abnormalities are um and  

28:33hypotonia are increased in frequency they’re like  you could say enriched in the syngap population  

28:40um but this is just a preliminary analysis  and we have many more of these red dots   that we have to look into and explore um  to try and see if we can identify other  

28:49unique features of syngap that maybe  haven’t previously been identified

28:58and in addition to using um this analysis  method to compare syngap to other types of  

29:04epilepsy we can make comparisons within  our syngap population to try to assess  

29:10for any genotype phenotype correlations so  here I’m showing the frequency of clinical  

29:15features in individuals with protein truncating  variants compared to those with missense variants  

29:23and in this preliminary analysis there seems to  be an enrichment of ataxia Tremor and atypical  

29:29absence seizures in those with protein truncating  variants compared to those with missense variants

29:41so as I’ve alluded to before one difficulty  in identifying these genotype phenotype  

29:46correlations is that the majority of the  pathogenic variants are protein truncating  

29:52um there are however many missense variants  that are classified as vus’s meaning variants  

29:58of Uncertain significance and some of these  individuals likely have syngap but they may not  

30:04receive a diagnosis um so this could hinder both  their clinical care and ongoing research efforts  

30:12so one thing we’re just starting to work on and  I’ll I’m just going to share a little bit with  

30:17you guys is trying to see if we can build a way  a predictor to predict weather of certain variant  

30:25of Uncertain significance is pathogenic or not and  this this effort is still in its infancy but what  

30:33we have done so far is we’re looking at several of  the existing scores that are used to predict the  

30:40harmfulness of a variant and here we’ve got the  scores plotted for sorry it’s kind of hard to see  

30:47for um in red pathogenic and lightly pathogenic  variants in blue benign and likely benign variants  

30:54and then in Gray is all of the US’s um and what  we can see at least preliminarily is that the  

31:02Revel score seems to get the best separation  between the benign and pathogenic variants  

31:09um but this is just a teaser and stay tuned  because we’re going to be working on this more

31:19so I’ve been talking a lot about how to use  data to reconstruct longitudinal phenotypes  

31:25which will be important for future clinical  trial design but one important aspect that’s  

31:30been missing is the lived experience  of individuals and their families   um and any trial that’s worth anything  needs to consider these factors when  

31:38creating outcome measures so here’s where  we enter the disease concept model these are  

31:46formal Frameworks designed to assess this lived  experience of individuals and their families

31:53so this is an incredible project by our own  Katie Rose Sullivan she’s a genetic counselor  

31:59with engine and a member of the Helbig lab and she  conducted semi-structured structured qualitative  

32:05interviews with 19 caregivers of 16 different  individuals with STX bp1 Related Disorders and  

32:11seven Healthcare professionals she then system  systematically coded them using in Vivo software  

32:17and then grouped Concepts into the domains of  symptoms symptom impact and caregiver impact

32:26um and here is just one of her  beautiful figures here you can see   um the frequency of these symptoms symptom  impacts and then caregiver impacts over time  

32:35and it’s really a beautiful and systematic way  to identify things like the effect of symptoms  

32:42on work or the emotional Care on totally  sorry the emotional toll on caregivers  

32:47um that previously hasn’t really been  studied or identified in such a rigorous way

32:58so there are a lot of interesting findings in  her study but one I just wanted to highlight   is this figure illustrating the differences in  the emphasis placed by Health Care Providers  

33:07and caregivers on different symptoms for example  providers were like less less likely to mention  

33:13things like socialization sleep and schooling  than the caregivers were and as a clinician  

33:20caring for children with rare genetic epilepsies  this is something that I really am trying to keep   in mind because the things that are important  to us and we think of going through a visit  

33:30aren’t really what’s important to families a  lot of the time so I think this is a really   important study to kind of get that point across  and while Katie Rose’s work was with the STX bp1  

33:44Community I’m very excited that we have um  Sydney Stewart a genetic counseling student  

33:50and a former genetic counseling assistant  with our program who’s going to be working   on this for singap so hopefully we’ll have  something to share soon about how this is going

34:04so finally I want to briefly talk  a bit about natural history studies  

34:10um as we’ll be starting one soon here at chop

34:16um as many of you are likely aware chop and  Pen received recently a 25 million dollar gift  

34:22to establish endd which is the center for  epilepsy and neurodevelopmental disorders  

34:29and Ed will be led by Ben Prosser whom many  of you know is a pen researcher but also an  

34:35STX bp1 parent to Lucy you can see Lucy here and  co-directors Bev Davidson and Ingo Helbig at Cha  

34:45so the goal of this project is to  accelerate research and therapies   for rare neurodevelopmental disorders and  part of this gift will be going to fund a  

34:55chop-based natural history studies for both  stxbp1 and syngap so this is very excited  

35:02um so I just want to share a little bit about  the content and structure of this new study

35:11um so here’s just an overview of our  end clinical team we have neurologists   developmental pediatricians um many a few  genetic counselors nurse practitioner we have  

35:22physical therapy we have occupational therapy  um we’re also going to include patient family   engagement clinical research coordinators  genetic counseling assistants and other  

35:32trainees such as residents and fellows so a  lot large team with broad areas of expertise

35:42um and then we are in the process  of finalizing the specifics for   the natural history study but I just  want to share with you guys a brief  

35:50overview of what we have planned and this  is the STX bp1 specific plan and timeline  

35:58um we’re still developing the syngap specific  one but it will follow a similar format but  

36:04some of the exact scales and assessments will  differ slightly just so we can better capture   some of the things that are more unique to  singap such as some of the behavioral concerns  

36:15but um what this will consist of is caregiver  questionnaires prior to the visit you can see here  

36:24um and then it’ll you’ll come for detailed visits  on site every six months and this will include  

36:30clinical assessments standardized developmental  skills um and a quantitative EEG as well

36:43um so for all the families on the call if you  are at all interested in coming to see us in  

36:48Philly to participate in our natural  history study please send us an email   um we’re probably going to have a new email  shortly that will be specifically for the  

36:58natural history study but for now you can reach  out to us at our epilepsygenetics at chop.edu  

37:04email we are starting to open up the schedule  for July and schedule patients and we would be  

37:10very excited if anyone on this call wanted to  come hang out with us um also if you are not  

37:18sure and you just want to learn more and have  any questions definitely please reach out as   well we would be very happy to answer them and I  can also answer any um during this call as well

37:32um so I went through those much quicker than I had  anticipated but on that note I just want to thank  

37:37our collaborators at Ambit and citizen for all  their efforts helping us Advance our knowledge of  

37:43syngap um Related Disorders and then to conclude  I want to thank Ingo and the rest of the Helbig  

37:50lab and especially chops team syngap over here  um and then also Mike and the rest of the syngap  

37:57research fund thank you for inviting me to speak  today and for all you do to helps and get families

38:06it was awesome this is awesome um thank you very  much Dr McKee that was I have a lot to say before  

38:13I say it though uh please everybody ask questions  I have I will be at Chop on Monday I have seen  

38:21how hard it is to get time out of people like Dr  McKee so this is your moment ask questions feel  

38:28free to ask her to go back to slides I’m expecting  questions on meds but I want to just make a couple  

38:33of observations while I’m blathering one I want  to thank Dr Holland schlach who has been on our  

38:39board and is part of our leadership team and  drove the ICD-10 code work and I will say is  

38:44driving the icd-11 code work which is not just  a rinse repeat it’s a whole other everything  

38:51and that is one of the ways in which srf is a  leader is we have the foresight to get on these  

38:56things because as you heard today and I please  please correct me Dr McKee if I misspeak here   having the ICD code is creates opportunities  for things like claims databases and Dr McKee  

39:08to figure out syngap so when I tell all of you to  please remind your clinician that we have an ICD  

39:15code and to take the ICD code to your meetings  this is why it matters number one number two  

39:21you saw the citizen data sign up for citizen it’s  free and it results in insights like what drug  

39:29we’ll let we give our kids because our kids  are resistant to drug and understanding that   the Depakote is one of the most effective well  not just dismissing all of the side effects is  

39:40a really interesting finding and I and I’m not a  clinician and I don’t give medical advice I will  

39:45say I’ve heard a lot of parents say we started  on Depakote we didn’t love it we weaned it down  

39:53and then when we went to no Depakote seizures came  back and then we went back to a limited Depakote   seizure stopped so there’s something there but me  saying that means nothing because I’m just a dad  

40:05but when we get data from Citizen and claims data  and we get someone like Dr McKee who’s a doctor  

40:11doctor then other doctors listen and our kids get  the right drugs right this is all about reducing  

40:19the suffering of our children and accelerating  the development of Therapies so when I pound the  

40:24table about ICD codes and citizen I’m just so I’m  just overwhelmed with excitement about what we saw  

40:30today because you’re seeing why this stuff matters  and I’m going to shut up and take questions um

40:37the point I just want to observe is that  that was a master class in a webinar it was  

40:43beautifully delivered it’s thoughtful  it was measured the slides were clean   I just an amazing presentation  and and thank you we’re really

40:55I’m at a conference right now and there’s  30 people in the Next Room who have kids  

41:00that are going to die in a couple years are going  to spend their life in a home and they’re nowhere   near as far as we are and I’m speaking at this  conference I am acutely aware that um we’re really  

41:13lucky I mean our life sucks and I hate what my  son has and I hate what it’s done to our family  

41:19but I realize we are so lucky to have people like  Dr McKee and Dr Helbig and Dr Ludwig and all the  

41:25other doctors who are on this webinar as well as  our families and it’s just really hope inducing  

41:31to see this presentation this isn’t just data  science this is going to reduce suffering and  

41:37get us closer so it matters and with that someone  please ask a question I will take Families First  

41:45um Ed is asking about the NHS how  many individuals are we looking for  

41:53so we’re starting slow to ramp up   um we are initially planning for two STX and one  syngap each week and it’s going to be on Thursdays  

42:05um but we are willing to schedule out long to  accommodate and then hopefully it’ll be two and  

42:10two soon and then we’ll see how the demand goes  and how our resources go in terms of ramping up  

42:17and you you kind of glossed over this not  a criticism just a just an observation   we’re not signing up for one visit we’re signing  up for a visit every six months for a period of  

42:27time is that right yes um yes so it the goal is  to come every six months once you enroll um and we  

42:36know we’re asking a lot of families to participate  in this especially people who aren’t local it  

42:41involves coming to Philly staying for probably at  least a day like at least one night depending on  

42:47how far away you’re coming from there’s caregiver  um questionnaires we’re going to ask you to fill  

42:52out prior to the visit it’s going to be a full  full day of Assessments clinical um assessments  

42:59ptot performing different developmental scales  and including quantitative EEG um but we’re really  

43:07really hopeful and optimistic that the data  we get from this will will make a difference

43:14and I’m sure there’s a we don’t need to get  into this now but if if there’s a family who   wants to participate and and the cost of the  travel and the accommodation is a problem  

43:23somewhere between chop and srf costs should  never be a barrier to um taking part in this  

43:30so you know please don’t let if you’re out  there and you’re like oh I want to do this   but I can’t afford it don’t let that be an issue  just just let us know that’s why the fund is here  

43:39um Jr in the in the um who is a mom in the  questions is first what ages will you be seeing  

43:46in your study second do you think you will add  Behavior specialist in your team from ages 8 to 13  

43:51my son’s behaviors went from cute to overwhelming  and destructive it might help I want to be I want  

43:57to know this is a good point that his behaviors  get got better as he got older that that’s  

44:04interesting yeah um so in terms of the the team  question we are planning to have a developmental  

44:10pediatrician who is someone who handles behaviors  prescribes behavioral medications and is kind  

44:16of our expert on that front I know every place  does it a little bit differently but behavioral  

44:22issues at chop at least tend to be split between  child psychiatry and Developmental Pediatrics  

44:28um so that’s why we are involving Dr Krueger in  this Clinic as well um and then in terms of Ages I  

44:36may actually have to get back to you guys on that  I believe it’s I think believe it’s anybody um but  

44:43I’m not sure if Sarah is has joined the call and  if she is she can text me to tell me if I’m wrong  

44:50um but otherwise I can get back to you guys on  that and I I should I also want to say um because  

44:58people are going to ask me okay she just texted  me it’s anyone anyone anyone okay good awesome uh  

45:05I want to also observe that srf is working with  Sydney and combined brain on the disease concept  

45:11model we are recruiting don’t worry as soon as  we get the ideal demographics for that population  

45:19Corey and Cali are going to be all over it so to  remain calm people um yeah I think behavior when  

45:27I saw that slide on the STX one and I looked  at the the caregiver assessments I mean you  

45:33you know this behavior is a is a bigger issue for  I think for singap and for STX yeah it tends it  

45:40tends to be from at least can you go back to  the drug slide yeah Citizen and Ambit please

45:53where is it um this one this  one the seizure yeah yeah

45:59how much should I read into LTG going to zero

46:05for the Ambit data you mean

46:11you know I don’t know we’d have to   we have to look at this more I don’t think you  really should read all that much into it it  

46:22yeah it seems to really just be from like 5  to 15 here that there’s a lot of Lamotrigine  

46:28um these are all pretty preliminary I wouldn’t  read too much into it it’s this is just telling  

46:34you the prescribing practices not really the  effectiveness right it’s just saying which meds  

46:40people are on hopefully that somewhat corresponds  to how effective they are hopefully we don’t have  

46:46a ton of people taking medications that aren’t  working for them um but in the comparative  

46:51effectiveness analyzes Lamotrigine still looks  pretty good so I I wouldn’t discount it because  

46:57it goes to zero here yeah Kevin if you’ve got  a question go ahead yeah can you hear me yeah  

47:06yeah okay yeah Julian so this is very good  and I’m sold on all this data it’s wonderful  

47:12but it seems to me like there’s I just would like  you to explain to me or help me understand better  

47:18what I think is a missing piece of data which is  we we go to the doctor regularly with our kids and  

47:27so there’s lots of great data that you can pull  from but our kids go to school or whatever similar  

47:36um thing they’re doing that’s not school all day  every day and you know so when I’m thinking about  

47:42the genotype phenotype correlation and you know  that looking at stuff on this Exon or that Exxon  

47:49are we missing crucial data and  not asking families what their   kid is doing all day and trying to  parse if there’s a difference there

48:01you mean in terms of like having teachers  assess how they’re doing in schools well no  

48:07no I mean not an assessment like that I mean  so the kids who have more language have they  

48:14been in a general Edge setting with an aide  and the other kids with less language have  

48:21been at home with ABA I’m not trying to I mean  I get that this is kind of a controversial thing  

48:28all of us have to figure out what’s best for  our families but to the extent that there’s   not a a drug or a treatment today are the choices  that we’re making with our kids you know is this  

48:41an appropriate place to be looking at some  of that data that’s what I’m curious about  

48:47yeah and I mean we we definitely should look  at that that’s a very important piece and we  

48:53haven’t yet um it is it is always something we  counsel right when you see patients in clinics  

49:01to maximize their development maximize their  therapies um it’s it’s a hard balance to  

49:07strike as I’m sure everyone on this call knows um  better than I do um for your family and your life  

49:15um and hopefully some of this could be captured  a little bit in the disease concept model too  

49:21um but it’s definitely a weakness that we don’t  have more data on this right now I just wonder if  

49:27you couldn’t add a couple of questions if you’re  going to be doing all this deep phenotyping and  

49:34trying to get all this information from families  yeah this be something useful and you know I  

49:39get that the data would be noisy because every  school different school is different every kid  

49:44is different I understand that but I just think  it’s an opportunity for more information yeah  

49:50during our clinical assessments we do ask like  how much therapy people are getting what sort of  

49:56school setting they’re in it’s not always recorded  in the most standardized way that probably means   it does get missed in some data extraction  processes depending on how you’re doing that  

50:07um but yeah I think that’s a great idea to have  a more explicit part in whatever data Gathering  

50:13instrument we’re using to really Define um  educational setting what therapies hours  

50:19of therapies all of those things yeah I I thanks  Kevin I I’m really glad you brought that up I want  

50:26to make three points one I do think IEPs and ABA  notes are a huge source of semi-standardized data  

50:35that we should turn the hell big McKee machine  on right a lot of PDFs there some robot could  

50:41look at and I and I think the specific question I  have is how many hours do our kids get written for  

50:49at what ages and then how many hours do they end  up using because there is such a thing as fatigue   and I’ll tell you a story about Tony when he was  three and having upstances that we were missing or  

50:58two having absences we were missing everyone  just thought he took after his dad a little   chubby a little lazy and so we just therapied  the hell out of it right and one day he went to  

51:08preschool and then he had PT and then he had St  and this poor little cingapian was exhausted and  

51:13he’s standing there while his Nanny’s talking  to his therapist talking to his dad and he had   his first convulsive seizure because we had wiped  this little boy out and it’s there’s a limit to  

51:24how much our kids can take sometimes and I think  the the guidance maximize the development give  

51:30them a lot of Therapies there is a natural  limit of what our kids can take and I think  

51:36refining our understanding of the  optimal balance between speech PT   OT and Aba it’s probably a topic for another  time but it’s something I would be happy to  

51:46support some research assistant digging into  because it’s there’s a trove of data there and  

51:52I think as usual Kevin’s onto something I I want  to just uh call out some of the questions here  

51:57um these are these are easy so I’ll let you  take blow through them Eric is asking is there   an age limit on the syngap study at chop Emily is  asking the the real good one here will there be  

52:09additional detail on seizure type versus effective  medication right as our kids go from the absences  

52:16to the convulsive sometimes to the drops did the  meds the meds change and then Olga is asking a  

52:23question that I kind of answered in text is  ambit’s data all based solely on the ICD-10 code  

52:30yes so great questions if I miss one of  them remind me Mike so first the age any  

52:36it’s anyone for syngap natural history study  Sarah just confirmed um we will see see everyone  

52:44um the next question was specific seizure  types um yes that is hugely important right  

52:53um especially when you’re looking at broader  populations certain seizure types respond in  

52:58opposite directions to certain medications right  um for for now we have not separated them out  

53:07um largely due to it’s always a trade-off  between your sample size if it gets too   small you lose statistical power um so you  you just don’t have the ability to detect  

53:17significant changes if you subdivide your groups  too small um but if we once we have more patients  

53:24more data then that’s definitely something we  should look at once we have the power to do it

53:30um and then the last question was Ambit oh ICD-10  so they are using they have to have a way to find  

53:37syngap patients in claims data so they use the  ICD-10 code to identify the patients and then  

53:44look at the other the medications and other um  clinical data that they have for those patients  

53:52but yes if they’re not tagged with an ICD-10  code then we would be missing those patients  

53:58awesome Jr you have so much are you  sure you don’t want to just talk   yeah yeah of course um thank you Mike so uh when  Kevin was talking and then Mike uh I I wrote a  

54:10sort of long and winding question so I’ll try to  just summarize it um in addition to adding on to  

54:16what Kevin was asking about which is you know  is there some sort of school or other type of  

54:21uh other other thing where our kids are doing all  day is there data from there that we can be using  

54:28um in addition to the things he mentioned there’s  also like teachers do a lot of toileting data  

54:33behavioral data that you can overlay with when  your meds are changing you know how many times   they try to elope food issues and and you know  personally we all know there’s good weeks and bad  

54:45weeks but we don’t we don’t kind of standardize  it we don’t really write it down right as a family  

54:51it’s a lot um but schools actually have a huge  amount of data and uh you know there’s a there’s  

54:57a letter that goes home every time my son’s  behavioral plan had to be implemented right  

55:02and so I get a letter and they say this happened  and this is what we did and we did it according to  

55:08plan right so there’s there’s actual data there  that um can be scraped and adding on to what  

55:17Mike said what did I say oh yeah that was  it that was it so I so I don’t know that  

55:23again Kevin asks is this the right place  to do this um I don’t know that you want to  

55:31add these Dimensions but you guys are doing  this you know you seem to have a lot of money   and you’re starting this and I think it’s really  good to think about these ideas and where can you  

55:41get data yeah I think I think this is actually  a really important point I’m really glad we’re  

55:47talking about it now um I do think some way  to capture how kids do in outside of Home  

55:55situations is because a lot of them spend a good  portion of their time there is really important  

56:01um some and you mentioned that they already  send home data to you which is great but I  

56:06imagine that would be different for  every person like not every school   was going to report the same things so it just  it becomes like a data standardization problem  

56:16um but we can talk as a team if there’s other even  it would be additional questionnaires um to like  

56:23assess more like teacher perspectives we already  we do that for the diagnosis of other conditions  

56:29like the one I’m thinking of on the top of my head  is like to diagnose ADHD we have a parents form  

56:35like the Vanderbilt scales you have the parent  scale and then you have a teacher scale and you   have to have them filled out from two different  situations because the features have to be  

56:45present in more than one situation right and so we  definitely take schools opinions and assessments  

56:52into consideration at other times in pediatric  neurology so why would we not try to do it here  

57:01I I realize a lot you’ve got the 25 million  dollars as an eye-catching number and I’m also   aware that Laura’s chunks of it are going  to be sucked up by beautiful Labs doing  

57:09incredible science if if srf needs to pay for a  research assistant to go through everyone’s IEPs  

57:17you should call us because I I really do  think there’s a lot of information there   and and I you know between yourself  and Dr Ludwig and others on this call  

57:26um I will be correct I’m happy to be corrected  but I I’ve been in the rare disease space now   for five years and I know very few diseases that  consistently I realize a lot of these kids have  

57:36behaviors but I don’t know another Gene where  behavior is such a consistent burden on families

57:46um I I want to call out a question um actually  while you’re on Jr you asked another question  

57:53about beautiful slides and impact do you want  to do that right yeah could you I think it’s a   few forward from this medication landscape slide  uh there’s there’s one that had sort of square  

58:04pictures I just keep going I’ll tell you when  we see it and I just I didn’t understand what   the data said so I’m sure and I and I did I did  understand what you’re talking about in a lot of  

58:13these slides it’s amazing there but I’m going  back yeah go back one this one so yeah can you  

58:19explain what the different what the different  colors mean and what like what’s the what’s  

58:25the zero to one density like yeah I didn’t  know what density meant I guess I understand   the age I don’t understand what density  means oh it’s just like the the proportion  

58:35um of people that like considered  that reported those symptoms   um it’s similar to like the medication landscape  as a density it’s just kind of like a proportion  

58:46okay so I guess so the so the top the top  ones are in basically nearly 100 percent  

58:53so no so no it’s um it’s not a so it’s a fraction  of like it’s like a piece of the pie so like the  

59:01behavior like comes down like this so it’s this  so like early on maybe like what is that they’re  

59:08not overlay they’re not overlaid there’s no  they’re not separate okay and this is STX this is   not syngap one that’s yeah right right no I just  didn’t even yeah I didn’t even know how to read it  

59:18though okay great great and then can you go to the  next slide because that one was pretty too yeah  

59:23I know these are the beautiful ones we’re gonna  we’re gonna do this first thing yeah but so what’s   this kind of plot called um oh my gosh what’s  this it’s not a spider plot right a Spiderman  

59:35um radar blanking on the name of it I call it a  radar plot yeah it kind of looks like a rainbow   eye Call It Whatever petals um yeah it’s the  it’s the it’s kind of like a circular histogram  

59:47um but it’s were you asking what it means or   yeah no it’s a circular histogram I got it now  yeah yeah and also I’m remembering it’s not it’s  

59:56not about it’s about stx51 so right okay thank  you so much and um I hope we don’t uh yeah I I  

1:00:04hope I don’t sound like I want more I just I’m so  excited about this and uh it’s really it’s really  

1:00:09amazing to hear what you’re doing and see what  you got so far thank you very much you’re welcome

1:00:17yeah Kevin is Kev there’s a beautiful chat going  on the Julie is commenting on the IEPs and Kevin  

1:00:23is observing that another really hard and like  incredibly meaningful data point that people get  

1:00:29lawyers to argue over is percentage of time in a  gen Ed versus an inclusive classroom and again n  

1:00:36of one the plural of anecdote is sometimes data  you know Tony went quickly from 90 10 80 20 gen  

1:00:43Ed inclusive Ed to exactly the opposite and our  kids do tend to be cute and so they’re ferocious  

1:00:51and and I you know I get in trouble for saying  that because the there’s parents of two-year-olds   on this call who are like oh what did Mike just  say but it’s true and I think we have to be honest  

1:01:00that you know this disease has a winding path  anyway um Yulia if you want to talk go for it  

1:01:06and then Emily do you want to ask your question  about blood or do you want me to just read I’ll   just I’ll yeah I’ll just read it this is Emily  can you hear me yeah sorry I am picking Hadley  

1:01:16up at school so it’s going to be she might hear  her in the background but I did have a question   um I recently had read some articles about blood  work um being used to identify seizures and  

1:01:25epilepsy and I was wondering if chop was involved  in that at all or if that’s something that they’re  

1:01:32familiar with and then also um if we’re tracking  any of the um physical attributes with the kids  

1:01:39I just I had I know we’ve as cyngap group talked  a little bit about some of the similarities that  

1:01:45the kids have but I was wondering if that  was something that is part of the research  

1:01:51oh okay um so the in terms of like blood  is it like biomarkers of epilepsy is that  

1:01:59what you’re asking about because we we are I don’t  I don’t exist I didn’t get the full details but  

1:02:07there was a reason I can’t remember exactly  where it was published but I don’t think it   was biomarkers I think it was like a lab test  that could be done like at an annual doctor’s  

1:02:18visit that could detect whether or not a seizure  had been registered I don’t know how that works  

1:02:23and I don’t know if that’s maybe something I got  wrong but um yeah I’m not I’m not sure about that  

1:02:31um okay but we are planning biomarker  development as part of our one of our souls

1:02:40and I have to look more into the other one I’m  not sure what that would be you can just you know

1:02:49get it yeah awesome and then  Julia do you want to say anything

1:02:56and then I just have one more doozy for Dr  McKee oh I have two doozies for Dr McKee um  

1:03:05okay yeah and then I’ll get to you Ashish I’m just  trying to privilege families first um doozy one

1:03:16because this is the question I get more than  anything right my kids just started seizing   I went to the doctor what drugs should I ask for  it is the question and I realize that it’s loaded  

1:03:27one and you’re a clinician and they’re clinicians  and every clinician’s got their favorite cocktail   what would you say speaking from the data and  then doozy two um it’s related to your bus slide  

1:03:40and the question is um anything what  are your thoughts on PTV versus missense  

1:03:46yeah you know you know I can talk about that ad  nauseam but okay um so first for first medication  

1:03:53Nuance at seizures um it it always depends on  like the whole patient right the rest of the  

1:04:00clinical scenario but based on the data I would I  would think Depakote would be a really good choice  

1:04:06um as a First Med come other combinations  I tend to lean to or like Depakote on fee  

1:04:13um Depakote Lamictal they tend to work they’re  sometimes tricky so a lot of people don’t like   to use them together but they tend to work well  together um keto also we didn’t have any um we  

1:04:24haven’t put some any comparative effectiveness of  Keto but that’s definitely something we’re going   to look at too tends to be effective yeah let  me let me put you on this I’m not push you but  

1:04:35let me just tell you the next question I get  after I say what you just said right because   I’m very quick to share that Depakote seems to  be particularly effective for the drops yeah  

1:04:46um like if a kid is having drops and not on  Depakote I tell them to go ask for depth code but   I also tell them and what I what I see again and  again with syngap kids is the neuro always writes  

1:04:56a lot of drug and something I learned in  college was with pretty much any substance  

1:05:02start low and go slow right and so  I encourage people to consider just  

1:05:09ramp up slightly to whatever they started you  want but I’m not a doctor and I don’t give   medical advice but with that said like very few  of our kids are successfully treated on one drug  

1:05:22and Depakote specifically while incredibly  effective with syngap at sometimes at the  

1:05:27prescribed dose tends to have an undesirable  side effects yeah so what is the what is the most  

1:05:34masterful way for a parent who is not a doctor  or a scientist to go back to their clinician and   be like I like this drug but how about instead  of a straight shot we go for a cocktail where  

1:05:45I get half of this and some of that like how  do we have that discussion and which slide or  

1:05:51poster of yours could be used to inform that  discussion like where is the chart that shows  

1:05:56that most kids are on multiple drugs as opposed to  a monotherapy yeah and that is that specific like  

1:06:04comparative effectiveness of combinations of meds  is something that I I haven’t included here we’ve  

1:06:10looked a little bit it’s it’s harder because  obviously the numbers go down too um because  

1:06:16to find people there’s so many unique there’s  so many there’s a lot of unique anti-seizure   medications and then when you factor in all the  possible combinations you get many more options  

1:06:27um but there’s so there’s the in terms of data  there’s not much in terms of like prescribing  

1:06:34philosophy if you can get away with one Med  that’s always always better goals should always  

1:06:41be no side effects no seizures it’s not always an  attainable goal but that should always be the goal  

1:06:47um and then if side effect men’s effective and  side effects are limiting you can always back  

1:06:53off on the dose and add another one that’s what  I tend to do um but it’s it’s always hard um  

1:07:00with different people prescribing I’m not going  to tell someone else’s doctor what to do I can  

1:07:06always I know it’s putting you in a really hard  spot and I apologize but it’s I’d rather you run   that spot than me and that’s what happens  right parents call me exhausted at the end  

1:07:15of the day and say it’s not working what do I  do so I appreciate you indulging my question  

1:07:20um and and while your deck literally brought me  to tears um it’d be amazing if you could visually  

1:07:30represent on one slide what percent of singapians  by age are on a mono versus a combo therapy yeah  

1:07:36no we think of what combos are are popping because  I I do that is the reality our kids are in and I  

1:07:42just wanna Echo one thing you said is back  off on the first drug and add a second one  

1:07:50as a someone in a front row seat to a lot  of these I see a lot of adding the second   but I don’t see a lot of backing off on  the first and and I and our kids end up  

1:08:02on a lot of drugs and and there’s a there’s  the the toxicity and the um the side effects  

1:08:07don’t get as managed as aggressively as the  seizures and I think that leads to a lot of   these behaviors but this is now I’m now I’m  really getting into deep Waters um missense bus  

1:08:20right so most of the variants in our population  that we’ve looked at with our data and citizen  

1:08:26are protein druncating variants we don’t have a  large enough number of missense and not a large  

1:08:33enough number of like recurrent missense where  many people have the same change to really get  

1:08:38a sense of are there unique subgroups based on  like particular missense variants there may well  

1:08:45be that we just don’t know yet because in other  other genetic conditions that is a common finding  

1:08:51that there are certain variants that have unique  clinical phenotypes we just haven’t been able to  

1:08:56identify it yet um in syngap and there are a lot  of Us’s um and hopefully as we have more patients  

1:09:07reported in the literature and more extensive  testing a lot of these will either be determined  

1:09:12to be pathogenic or benign and cut down on some of  the US’s but it’s it’s always an ongoing process

1:09:21all right um other family questions text me call  me message me before I open up to the non-families  

1:09:32as she had a Ashish has questions about long  read sequencing I don’t know who she is but I  

1:09:38think he’s a geneticist um they’re at the top  of the Q a if you want to take those quickly  

1:09:45me just end this slide so I can see the Q a   you’re gonna stop sharing your screen yeah oh wait  that’s what I have to do I have to stop sharing  

1:09:56where’s my mouse it’s just always  risky to share this group okay

1:10:04okay another question here oh there’s chat and a q a okay ignore  the chat do you think longread sequencing  

1:10:13could reveal further insights into  genotype phenotype associations

1:10:19I mean uh so is everyone on the call people  probably aren’t all familiar with long read  

1:10:25sequencing where you you use it’s basically  just Technologies to like look sequence longer  

1:10:33um kind of essentially what it is but  longer stretches of DNA at a time I  

1:10:39I don’t know that it would because we already at  this point we already have um like we we have a  

1:10:47problem where we know what the change is and we  just don’t we don’t have a good sense of genotypes  

1:10:53and phenotype I think what would help the best  is more patience like identifying more singapians  

1:11:01getting clinical detailed information on them and  understanding and knowing what their Marion is and  

1:11:07then we can start to detect patterns but it’s  really it’s hard to do that when you only have   like one or two people with a given misdance  variant how do we how do we know whether it’s  

1:11:18just the natural variability of syngap or whether  it’s actually something specific to that variant

1:11:25all right we’re way over time I just want to  read uh Julia didn’t want to jump on the call   but you will meet her on Monday by the way  and um she said thank you Dr McKee this was  

1:11:35fantastic as scientists we really appreciate  the important research of singap parents it   gives us a glimpse of Hope which means a world  thank you bye couldn’t have said it better so  

1:11:45with that really loved this presentation  and and thank you and we’ll see you Monday  

1:11:51yeah thank you so much for having me and I’m  happy to take any other questions offline great