This document presents a Delphi forecast on the future of gene therapy aimed at curing Down syndrome (DS), estimating that potential cures may emerge between 2045 and 2050. It discusses the significant medical costs associated with DS and the ethical, legal, and social challenges that hinder gene editing advancements. Predictions indicate that by 2030 to 2039, 6.7% to 15% of genetic disorders could be treated, with the cost-effectiveness of therapies being a crucial factor for their adoption.

1. 1
Delphi Forecast for Curing Down Syndrome
Andrew James Wilhelm
Vanderbilt University
2301 Vanderbilt Place
Nashville, TN 37235
+1 (724) 900-0280
andrew.wilhelm@vanderbilt.edu
ABSTRACT
The purpose of this Delphi forecast is to provide a future timeline
for gene therapy, in regards to curing Down Syndrome (DS). The
underlying need for this innovation is the high medical cost
associated with DS childcare. Opposition involves legal, social
and ethical issues, the largest being legal precedence restricting
research on human gametes. Relevant companies include a
number of healthcare providers and government agencies. These
organizations are primarily concerned with the feasibility of gene
editing and how to reduce cost should new cures be discovered.
When evaluating the cure to DS, two different variations of the
Delphi methods are incorporated. This research identifies critical
topics related to the growth of genetic engineering and yields
predictions of upcoming capabilities. Analysis of both the entire
field of gene therapy, and the subset of chromosomal diseases
prevention, gives insight to the potential preclusion. This forecast
predicts that between 6.7% and 15% of genetic disorders are
treated between the years 2030 and 2039. When looking at the
cure for DS, introduction will not occur until 2045 to 2050.
Keywords
Gene therapy, CRISPR, Down syndrome, TechCast Global,
RAND Delphi method
1. INTRODUCTION
Since the dawn of mankind, existence has been facilitated by
biological reproduction. While not as efficient as asexual division,
it provides diversity by combining characteristics of two orgasms
into one unique structure. This is crucial to species development
and drives desire for understanding of the origin. Looking at
primordial deoxyribonucleic acid (DNA) yields insight into the
existence of Mitochondrial Eve. This individual serves as the
basis of our ancestral DNA history and is the maternal
primogenitor to all. Though considered pure, the genetic material
of Mitochondrial Eve is the most vulnerable to imperfections [1].
Years of evolution has resulted in a more stable genome that
represents modern humans. Given this overview, it is evident
progress has been limited by natural selection [1]. However,
advances in technology enable the ability to dictate this selection
through gene editing.
Moving into the applications of genetic engineering, disease
prevention is at the forefront of discussion. Reduction in any
lifecycle stage is ideal, but the most beneficial is during the first
year of life [2]. At this phase, healthcare cost is more than any
other age by a ratio of three to one, typically due to birth defects
[2][3]. Birth defects are a form of illness that effect the structural
or functional abilities of newborn infants. These are common,
occurring 1 in every 33 births, and cost an estimated $2.6 billion
annually in the United States (US) [2]. A breakdown of common
birth defects is shown in Figure 1.
Figure 1. Statistics on birth defects [2]
This depicts chromosomal disorders as the most frequent birth
defect, of which is Trisomy 21. The odds of this mutation are
about 1 in 700. As such, forecast scope will focus on this disorder,
also known as Down Syndrome (DS). DS is caused by an
additional 21st chromosome in the human genome. Individuals
with DS experience both mental and physical challenges when
compared to those with healthy genetic structures [3]. Given these
complications, medical expenses are higher. Figure 2 shows the
difference in healthcare cost for those with DS in relation to a
control group.
Figure 2. Cost of DS compared to control [3]
With a clear need presented by healthcare savings, several
hinderances stifle the progress of gene therapy. The first reflects
the “ethical-legal dilemmas and different social risks” associated
with genetic engineering [4]. Although controversial and loosely
76%
19%
4% 1%Types of Birth Defects
Chromosomal (76%)
Single Gene (19%)
Teratogen (4%)
Twinning (1%)
46%
22%
11%
21%
Chromosomal Disorders Expanded
Trisomy 21 (46%)
Structure (22%)
Trisomy 18 (11%)
Other (21%)
1
10
100
1000
10000
100000
0 to 1 1 to 3 3 to 5 5 to 13 13 to 18
Cost(USDollars)
Age (Years)
DS Group Control Group

2. 2
defined, Food and Drug Administration (FDA) regulations
prohibit modification of human embryos [5]. Should this
technology be made legal, negative social connotations will still
pose a risk to sector growth.
Following understanding of the underlying factors driving gene
therapy, identification of industry beneficiaries helps model the
impact of expansion. Companies directly influenced by the cure to
DS include healthcare organizations such as UnitedHealthcare,
Health Care Service Corporation, and Cigna. Specifically, this
forecast addresses Richard Migliori, Chief Medical Officer of
UnitedHealthcare. The purpose is to help Dr. Migliori answer
questions pertaining to date of inception, cost, and organization
changes required by cure introduction. To address these, several
different Delphi based forecast are conducted
2. DELPHI METHODOLOGY
When evaluating the opportunity for a cost-effective DS
treatment, this research relies on the use of expert knowledge.
Several different ideologies are taken into account and contribute
to a multilateral assessment. Assumptions and limitations revolve
around the research scope. No consideration is given to the
earning potential of DS individuals during adulthood.
Furthermore, only medical expenses related to the cost of DS are
studied. This does not include any additional financial
implications experienced by caregivers. Finally, the economic
aspect of DS is the single factor examined. Social drivers, such as
group motivation or empowerment, are deemed negligible.
2.1 RAND Delphi Method
Although expensive and time consuming, the RAND Corporation
Delphi method is comprised of a four-round process, ideal for
eliciting expert opinion. This judgmental technique aims to reduce
the cognitive bias associated with groupthink, while gaining the
benefits of group knowledge. Round one begins with expert
selection and an unstructured questionnaire. Designed to identify
future events of interest, the responses are consolidated into a
single list. This is distributed to participants in the subsequent
second round, where event timeframes and likelihoods are
approximated. Given the feedback, round three details a statistical
analysis of the results, including medians and quartiles, which is
returned to the group. Participants are asked to adjust their
forecast, based on the new information, or provide justification if
it lies in the outer quartiles [6]. Round four is iterative and
concludes when the panel reaches consensus, typically after three
repetitions [7].
2.2 ScienceDirect Approach
Once the archetype of Delphi methodology is understood, the
implementation to gene editing is described by the ScienceDirect
Journal of Technology Forecasting and Social Change [8]. This
strategy focused on gathering information from highly qualified
sources and sought to select ideal panel members. Constructed
from experts with experience in performing invitro fertilization
(IVF) and preimplantation genetic diagnosis (PGD), participants
included those with skill in assisted reproductive technologies
(ART). With the highest IVF aided births worldwide, 4.7% and
8.0% respectively, Israel and Spain localized the panelist pool [8].
25 voluntary participants with occupations ranging from
gynecologists to geneticists were selected. Of these, 13 were from
Spain, 12 were from Israel, and had an average of 19 years of
experience in ART [8]. Though knowledgeable, lack of
socioeconomic diversity within the group may skews research
results and not directly correlate to sector growth worldwide.
Upon panel assembly, unstructured, in-depth interviews were
conducted to “guide the design of the Delphi questionnaire” [8].
The intent was to identify topics related to ART growth, over the
next 20 years. From here, events were combined and given to
panelist for evaluation, which ranked both progress and likeliness
on a 10-point scale. This was repeated twice, regardless of
agreement, due to limited expert availability [8]. Results
contained the 10-point scale average for all subjects covered, in
conjunction with the standard deviation. A standard deviation less
than two was desirable and considered reasonable consensus [8].
2.3 TechCast Approach
Another variation of traditional Delphi technique is the TechCast
Project. Based at George Washington University, TechCast is an
online system that connects experts from around the world. The
concept is to combine background trends with expert judgment to
predict breakthroughs in all fields [9]. Although experts are not
necessarily involved in the industry investigated, they include
roughly 130 CEOs, scientists, and consultants. Working in hand
with empirical data, the process highlights “how pooling the tacit
knowledge and collective intelligence of 130 good minds can
create forecast[s] that are remarkably prescient” [9]. Along with
this, the TechCast Project is ongoing and looks to continuously
improve predictions as new information is released. The constant
iterative progression proves useful when approximating
technological evolution.
When related to the field of genetic engineering, TechCast
provides both a timeframe to maturation and confidence score.
While not as detailed as traditional Delphi methods, the
information illustrates a general overview of growth perspective.
Quantitate results include average predictions and standard
deviation for the entire field of gene therapy. Most importantly,
the distinguishing benefit is the inclusion of cost analysis.
Conversely, a major limitation is improper expert selection and
feedback credibility.
3. FORECAST RESULTS
3.1 Gene Therapy Industry
The collaboration of several Delphi methodologies allows for
estimation of the gene therapy industry. The first aspect discussed
is that of the entire sector. Healthy fundamentals must be
established before more elaborate disease prevention capabilities,
such as a cure for DS, are made available. Looking at the report
generated by TechCast, experts approximate that “genetic therapy
is able to cure 15[%] of inherited diseases” by the year 2030 [10].
The distribution of predictions and confidence scores are shown in
Figure 3.
Figure 3. TechCast predictions and confidence [10]
In hand with this, ScienceDirect provides similar numbers, but are
slightly less optimistic. They state that 16.5% of total births will
be IVF, and of those, 41% will undergo PGD. These predictions

3. 3
entail potentially curing 6.7% of inherited diseases in the year
2039. Also, panelists claim that using “PGD to prevent
multifactorial diseases could produce some health advantages”
[8]. While not necessarily chromosomal disorders, multifactorial
disease reduction leads to more advanced solutions.
3.2 Curing Down Syndrome
Given the consideration of multifactorial diseases, the possible
treatment for DS is theorized. As previously stated, DS is a
chromosomal disorder that occurs during gamete formation early
in fetal development [2]. No direct mention of DS is made in any
of the forecast material researched, even with it being the leading
genetic disorder. This indicates technology needed for elimination
may be beyond the time horizon specified. However,
ScienceDirect did provide predictions on several topics pertaining
to genetic engineering.
Figure 4. Genetic engineering predictions [8]
Figure 4 shows the likelihood of progress in three subjects
relevant to DS. Experts have a slightly optimistic opinion
regarding chromosomal disease preclusion in the next 20 years.
Furthermore, the chance of success is higher than that of
multifactorial improvements, which are part of expected PGD
capabilities. This is an indicator of anticipated growth by the year
2039.
A decisive aspect of the treatment is cost effectiveness. When
looking at medical savings, expense of DS childcare must be
greater than cure implementation. If the prevention cost is higher,
it will not be adopted. Several FDA approved gene therapy
treatments have struggled due to high cost. However, a gene
editing technique called CRISPR/Cas9 has made it possible to edit
any genome, at a substantial price reduction [10]. Should this
technology evolve, it may mitigate cost prohibiting aspects of DS
prevention, making the solution accessible.
4. CONCLUSIONS
The information provided allows for a forecast of the gene therapy
sector, especially the cure for DS. This assessment is based on
analysis of the economic factors driving medicine manufacture.
Healthcare for a DS child is nearly 10 times that of a healthy
individual [3]. While this explains the need for disease
elimination, it is susceptible to drawbacks. The largest of which is
legal precedence in the US prohibiting genetic experimentation on
human embryos [5]. This limitation, enforced by the FDA, is
loosely defined and may be removed from law shortly. Social and
ethical issues also raise concern when looking into the growth of
genome modification. These factors are minor in comparison and
will not drastically influence scientific progress. To get a better
understanding of development in gene editing, two different
Delphi approaches, administered by ScienceDirect and TechCast
Global, are studied. Due to the joint assessment, limitations
associated with each individual method are reduced. The narrow
demographic pool of experts in ScienceDirect is offset by the
overreaching TechCast participants. This improves confidence in
the evaluation. The first topic considered is the industry as a
whole. Both studies made predictions about curing genetic
disorders and estimated between a 6.7% and 15% reduction by the
year 2030 and 2039 [8][10]. When looking at DS directly, the
prediction becomes more complex. ScienceDirect acknowledges
above average potential for the evolution of genetic engineering.
It also expects a decrease in the number of genetic disorders,
through PGD, by 2039 [8]. Although DS is never directly
evaluated, an opportunity for advancement is evident.
In summary, Dr. Migliori will need to assess several key findings
of this forecast. First, addressing the date of inception, the cure to
DS will be possible between the years 2045 and 2050. When
considering the cost, it assumes the adoption of CRISPR
technology will reduce gene therapy expense to a point where
disease elimination is worthwhile [10]. Finally, concerning
organization changes, healthcare companies must maintain all
facilities oriented to DS management. The timeframe is too distant
and does not present an advantageous opportunity.
5. REFERENCES
[1] NORRIS, J. 2011. Genomes shed light on human evolution,
leading scientists to search for more subtle clues.
https://www.ucsf.edu/news/2011/03/9450/genomes-shed-
light-human-evolution-and-selective-sweeps.
[2] FELDKAMP, M.L., CAREY, J.C., BYRNE, J.L.B.,
KRIKOV, S., AND BOTTO, L.D. 2017. Etiology and
clinical presentation of birth defects: Population based study.
The BMJ, 2017, 357:j2249
[3] CENTERS FOR DISEASE CONTROL AND
PREVENTION. 2019. https://www.cdc.gov/ncbddd/birth
defects/downsyndrome.html.
[4] KAGELEIRY, A., SAMUELSON, D., DUH, M.S.,
LEFEBVRE, P., CAMPBELL, J., AND SKOTKO, B.G.
2016. Out-of-pocket medical costs and third-party healthcare
cost for children with Down Syndrome. American Journal of
Medical Genetics, Part A, A:1-11.
[5] KAISER, J. 2019. Update: House spending panel restores
U.S. ban on gene-edited babies. https://www.sciencemag.org
/news/2019/06/update-house-spending-panel-restores-us-
ban-gene-edited-babies.
[6] VAN SCHAACK, A.J. 2020. The Delphi method.
Unpublished manuscript, Vanderbilt University, Nashville,
TN.
[7] ROWE, G., AND WRIGHT, G. 2001. The role of the Delphi
Technique. In Principles of Forecasting, J. ARMSTRONG,
Ed., Kluwer Academic, Boston, MA, 125-144.
[8] ALON, I., GUIMON, J., AND URBANOS-GARRIDO, R.
2019. What to expect from assisted reproductive
technologies? Experts’ forecasts for the next two decades.
Technological Forecasting & Social Change, 148, 119722.
[9] HALAL, W.E. 2013. Forecasting the technology revolution:
Results and learnings from the TechCast Project.
Technological Forecasting & Social Change, 80, 1635-1643.
[10] DAVIES, O., AND FLETCHER, A. 2018. Forecast report
on gene therapy. Unpublished manuscript, TechCast Global,
Washington, D.C.