Hey Everyone, wanted to share a quick post compiling a few discussions I've had regarding BAT 3-Yr OS. In addition, I finally got some time to do a deeper dive into OPTI-AML (the most recent Ven/Aza data we have, from the results published last month, June 2026)

When it comes to OPTI-AML/Fatima 2026, the results of which were shared in June 2026, https://ascopubs.org/doi/10.1200/JCO.2026.44.16_suppl.6525

"HMA plus venetoclax for 7- vs 14- vs 21- vs 28-day cycles in newly-diagnosed acute myeloid leukemia: ELN- and Mayo Genetic Risk–stratified analysis in 540 patients."

It was an n of 540, new-diagnosed (frontline, unfit), 7 vs 14 vs 21 vs 28‑day Ven duration comparison

It directly reinforces the toxicities of Aza/Ven (this is current recent data). It's interesting because this demonstrates/shows that real-world attempts to mitigate Aza/Ven toxicity (like dropping to 14 days) lead to inferior remission rates. The OPTI-AML trial (Frontline patients) they are going over here shows that the efficacy baseline of standard BAT remains restricted by its toxicities. The 28-day schedule is difficult for older patients to sustain sequentially without experiencing severe cytopenias (lower than normal mature blood cells). A 28-day schedule is necessary, but it remains a punishing double-edged sword of toxicity.

Patients cannot stay on continuous 28-day Aza/Ven indefinitely without significant complications, yet dropping the dose risks early relapse.

Feature	REGAL	QUAZAR (AML‑001)	Aza/Ven R / R across studies	Kurosawa 2010	VIALE‑M
Design	Ph3 RCT, open‑label	Ph3 RCT, double‑blind	Retrospective (Mayo)	Retrospective (Japan)	Ph3 RCT, double‑blind
N	126	472	N/A	CR2/no‑HCT subgroups (n=14-82)	112 (of 360, terminated)
Disease state	CR2 (remission)	CR1 (remission)	active failure (R/R)	CR2 (remission)	CR1/CRi (remission)
Remission line	2nd	1st	none (failed frontline)	2nd	1st
Refractory	0% (all CR2)	0% (all CR1)	N/A	0% (all CR2)	0% (all CR1)
Median age	67 (57% greater than or equal to 65)	68 (greater than or equal to 55)	75	53 (16-70), youngest	CR1 maint (65)
Cytogenetic mix	depleted of adverse (CR2 selects)	mixed	complex	reported by risk group	mixed
TP53	5-10%	low-moderate	about 29%	cytogenetic‑era (pre‑TP53)	low-moderate
Setting	maintenance	maintenance	salvage (active disease)	observational (no‑HCT)	maintenance
Transplant	ineligible (0%)	non‑candidates (0%)	about 3.7% (Gangat 2023 Haematologica is a great resource)	HCT vs no‑HCT subgroups	not to SCT (0%)
Primary endpoint	OS	OS	(observational)	(prognostic factors)	RFS
Reference arm	BAT (inv. choice)	placebo	(salvage regimens)	no‑HCT, by cytogenetics	oral‑aza (Onureg)
Reference mOS	BAT 8-13 True Onset mOS	placebo 14.8, Onureg 24.7	4 mo (failure), SCT high (only near-cure/cure for AML)	by cytogenetics	oral‑aza 26.7 design
Reference 3‑yr OS	13-19% approximation	placebo 25%, Onureg 40%	5% (SCT subset may be 33%, given 2-Yr OS is about 61%)	CBF 64%, intermediate 19%,adverse 35%	42% design (no readout)
Status	pending (78/80)	positive (approved)	real‑world	retrospective (2010)	failed/terminated

I'll share a lot of what I think are useful insights from looking into each of these. But to start with for Kurosawa, Kurosawa is the closest map to REGAL, same CR2/no‑transplant setting, but younger, so it reads high. Its intermediate‑risk no‑HCT arm (19%, n=82, median age 53) is the single best analog to REGAL's BAT bulk. Age‑adjust it down for REGAL's 67‑year‑old population and you land at 13-16%, which is where the most biologically plausible actual fits land as well.

One thing you'll notice is its favorable‑CBF tail (64-78%) is the durable subgroup, these are the CBF patients.

group	3‑yr OS	n
inv(16)	78%	14
t(8,21)	53%	18
intermediate	19%	82
unfavorable	(35%, n=18 - small‑N outlier)	18

So the n=14 is inv(16) specifically, the single best CBF subtype, not all of CBF. Full core‑binding‑factor = inv(16) (n=14, 78%) + t(8,21) (n=18, 53%) = n=32, blended 64%.

CBF patients are 10 to 15% of patients in AML, but almost half of that (7% of CBF patients) are over the age of 65, and REGAL is not enriched for it. Sharing a link to the in-depth stress-testing/impossible scenario stress-testing I did for CBF patients to the actual fits:

https://www.reddit.com/r/sellaslifesciences/comments/1u697pa/comment/os2lpor/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

The results of that in-depth stress testing/impossible scenario stress testing, showed it is nothing to worry about at all, the margin of safety is gigantic when it comes to CBF risk.

The median age of REGAL skews higher than Kurosawa, so that lowers the volume of CBF patients and the age has an impact

https://preview.redd.it/sai70tdeqgbh1.png?width=973&format=png&auto=webp&s=f2b285f513ba65d2ea3d5aeffa5fdbc45a1987e5

You can see the median age in REGAL from this link, just open it and search for age:

https://www.clinicaltrialsregister.eu/ctr-search/trial/2019-004134-42/FR

REGAL is much older than Kurosawa. Kurosawa's median age was 53 (range 16-70). REGAL, per the EU register shared, is 50 patients 18-64 vs 66 patients greater than or equal to 65, i.e. 57% are greater than or equal to 65, median of about 67. Older AML has fewer favorable‑cytogenetics patients and worse survival within every group. Kurosawa's numbers are therefore an over‑estimate applied to REGAL, and Kurosawa even caps at 70, so it barely includes REGAL's oldest tier.

In addition, this is a really helpful view:

control arm	setting	3‑yr OS
VIALE‑M oral‑aza (design)	CR1, maintenance	42% (no readout)
QUAZAR Onureg	CR1, maintenance	40%
QUAZAR placebo	CR1, no active drug, older	25%, upper bound for a CR2 arm
Kurosawa CR2 intermediate	CR2, younger (53), no‑HCT	19%
REGAL BAT Approximation	CR2, older (67), best‑available	Approximately 13-19%
Kurosawa CR2 CBF favorable	CR2, younger, no‑HCT	19% non-CBF, 64% CBF patients (which make up 15% of AML, and 7% of patients over 65)

Looking at this, one may be able to conclude from this that the setting, not the drug, drives the durable tail. Deriving each control's 3‑yr OS from its median + cure fraction.

From this, one can conclude that CR1 controls cluster at 25-42%, CR2 controls at 13-19%. A CR2 patient has already relapsed once, the durable cure tail is thinner.

Another really useful comparator is QUAZAR's placebo arm, CR1, older (median 68), transplant‑ineligible, no active maintenance and 25% 3‑Yr OS. REGAL's BAT is the CR2 version of that same kind of patient, a worse prognostic setting. So QUAZAR placebo is essentially an upper bound, REGAL's BAT 3‑yr OS should sit below 25%, which is exactly where the CR2 comparators (Kurosawa intermediate 19%, adjusted down for age) and the fits (13-19%) land

For REGAL's BAT 3‑yr OS to reach 25%, a CR2 arm would have to roughly equal a CR1 arm (QUAZAR placebo), and match it despite being older than Kurosawa's cohort too. Every dataset here says CR2 < CR1 and older < younger. That's why 25%+ is the upper edge, not the center, it requires REGAL's twice‑relapsed, elderly, transplant‑ineligible population to survive like a first‑remission population. The only way there is a large favorable‑CBF fraction, the exact subgroup that's young, fit, and transplant‑eligible, so screened out of REGAL.

In addition, the results for Ven+HMA/Ven+Aza in R / R in a similar age population as to REGAL in R / R, 3-Yr OS is about 5%.

In fact, although R / R is different than CR2 (meaning R / R is worse), in Ven / Aza R / R patients, from previous data, only about 3.7% transitioning to transplant (Gangat 2023 Haematologica is a great resource along with other Ven / Aza R / R studies).

In QUAZAR, the transplant rate for Onureg's arm was 6.3% and for placebo, was 13.7%. QUAZAR's placebo arm sent 13.7% of patients to subsequent transplant, more than double the Onureg arm (6.3%), because placebo patients relapsed more and went on to salvage + SCT. Those transplants inflate the placebo 25% 3‑yr OS. So the "pure, no‑transplant" QUAZAR‑placebo 3‑yr OS is actually below 25%, and REGAL's BAT CR2 and 0% transplant at entry by design, means the 3-Yr OS in BAT likely sits below that. When you strip how the transplant-inflation, it's likely a 20% 3-Yr OS for QUAZAR-placebo, and REGAL's transplant-ineligible at entry CR2 arm is likely lower. Although, I would not read into this too much, since QUAZAR was also not eligible for transplant. The useful view here is why the transplant rate may be equal or less than QUAZAR for REGAL, given what we see above how the setting, not the drug, may drive the durable tail.

The impossible scenrios/worst-case scenarios transplant-tail stress-test provides an enormous margin of safety for a transplant-tail risk, when looking at the actual fits, resharing that here:

https://www.reddit.com/r/sellaslifesciences/comments/1uca7s3/comment/otmmarm/?context=3&utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

https://www.reddit.com/r/sellaslifesciences/comments/1ubovjk/comment/oszntjv/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

So, given 3-Yr OS from R / R is about 5%, and CR1 is 25% based on QUAZAR, in the middle being 13% to 19% may be something one can conclude in terms of biology.

As we just went over, from previous data, only about 3.7% of Ven/Aza R / R make it to transplant (Gangat 2023 Haematologica is a great resource, as well as other studies), and those that do have maybe 3-year OS of 33%, since 2-Yr OS after transplant is 61%, dramatically better than the non-transplanted majority. This reinforces that REGAL's transplant-ineligible population has a hard ceiling on long-term survival. It only becomes a problem if 3-Yr OS in BAT at any IRM is above 31%, or if a combination of a BAT IRM of 18/19 occurs with a 3-Yr OS of 26%+

Of course, in CR2 that number will be higher than 3.7% due to the healthier patients than R / R, but the age range is very similar, so what Dr. Tsirigotis said of a negligible transplant tail is likely the case, and the comparisons we just went through point to about 13%-19% for BAT 3-Yr OS.

Sharing the exact words from Dr. Tsirigotis from the forwarded email on April 30th, a few months ago:

"One think i can say for sure is that the long term survival for patients in CR2 without consolidation with Allo-SCT is negligible. On the other hand a significant percentage of patients in CR2 who proceed in Allo-SCT can enjoy long survival and even cure in many cases (again the range of percentages is wide and depends on many factors). In Greece all patients up to the age of 70-years are considered eligible for Allo-SCT unless they have significant comorbidities or poor performance status. We are very reluctant to proceed in Allo-SCT in patients above the age of 70 and this patient population that actually receive a transplant constitute a highly selected group."

Now, to conclude I wanted to share the actual fits to 60/72/78/and 80th as a constraint as of July 3rd, 2026. Given what we just went over about the biological likelihood 3-Yr OS range, you can see which actual fits align and what the results would be, for GPS alive / BAT alive, HR, 3-Yr OS, etc. for each arm, and at the 80th along with at IA.

When you look at these, you'll see why I mentioned it doesn't really matter what BAT IRM is with BAT 3-Yr OS being 13% to 19%

BAT IRM 11

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	33%	49%	35	24.3	0.393	0.558	0.598	[0.39,0.93]	25.9	40.9	19.8	26.6	61%
0.5	29%	51%	37	23.7	0.358	0.447	0.479	[0.31,0.74]	24.5	41.9	16.9	29.9	90%
0.6	24%	55%	42	23.7	0.318	0.362	0.387	[0.25,0.60]	23.1	43.3	14.2	32.7	99%
0.7	20%	59%	49	23.6	0.282	0.295	0.315	[0.20,0.49]	21.7	44.7	11.7	35.2	100%
0.8	17%	62%	56	23.4	0.248	0.242	0.259	[0.17,0.40]	20.4	46.1	9.4	37.6	100%
0.9	13%	66%	64	23.2	0.218	0.201	0.215	[0.14,0.33]	19.1	47.4	7.3	39.6	100%
1	10%	68%	72	23	0.192	0.17	0.182	[0.12,0.28]	17.9	48.6	5.6	41.4	100%
1.1	8%	71%	79	22.7	0.169	0.146	0.156	[0.10,0.24]	16.8	49.8	4.2	42.9	100%
1.2	6%	72%	84	22.4	0.149	0.128	0.137	[0.09,0.21]	15.8	50.8	3.1	44.1	100%
1.3	4%	74%	88	22.1	0.132	0.115	0.123	[0.08,0.19]	14.9	51.8	2.2	45.1	100%

BAT IRM 12

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	34%	47%	33	24.3	0.429	0.606	0.648	[0.42,1.01]	26.7	40.1	20.6	25.9	47%
0.5	30%	49%	35	23.7	0.402	0.496	0.531	[0.34,0.82]	25.5	40.9	17.9	29	79%
0.6	26%	53%	40	23.7	0.366	0.41	0.438	[0.28,0.68]	24.3	42.2	15.3	31.5	95%
0.7	22%	57%	46	23.6	0.331	0.339	0.363	[0.23,0.56]	23.1	43.4	12.9	34	99%
0.8	19%	60%	53	23.5	0.299	0.283	0.302	[0.20,0.47]	21.9	44.6	10.6	36.3	100%
0.9	16%	63%	61	23.3	0.27	0.238	0.254	[0.16,0.39]	20.8	45.7	8.6	38.4	100%
1	13%	66%	70	23.1	0.243	0.202	0.216	[0.14,0.34]	19.7	46.8	6.8	40.2	100%
1.1	10%	69%	79	22.8	0.219	0.175	0.187	[0.12,0.29]	18.7	47.9	5.3	41.8	100%
1.2	7%	71%	87	22.6	0.197	0.153	0.164	[0.11,0.25]	17.8	48.9	4.1	43.1	100%
1.3	6%	72%	93	22.3	0.178	0.137	0.146	[0.09,0.23]	16.9	49.8	3.1	44.2	100%

BAT IRM 13

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	35%	46%	32	24.2	0.465	0.653	0.699	[0.45,1.08]	27.4	39.4	21.3	25.2	34%
0.5	32%	48%	34	23.7	0.445	0.546	0.584	[0.38,0.91]	26.4	40	18.8	28.1	65%
0.6	28%	52%	38	23.7	0.414	0.459	0.491	[0.32,0.76]	25.3	41.1	16.3	30.5	88%
0.7	24%	55%	43	23.7	0.384	0.386	0.413	[0.27,0.64]	24.3	42.1	14	32.9	97%
0.8	21%	58%	50	23.5	0.354	0.326	0.349	[0.23,0.54]	23.3	43.2	11.8	35.1	100%
0.9	18%	61%	58	23.4	0.326	0.278	0.297	[0.19,0.46]	22.3	44.2	9.8	37.1	100%
1	15%	64%	67	23.2	0.3	0.238	0.255	[0.16,0.40]	21.4	45.1	8	39	100%
1.1	12%	66%	77	23	0.275	0.207	0.221	[0.14,0.34]	20.5	46.1	6.5	40.6	100%
1.2	10%	69%	88	22.7	0.253	0.182	0.195	[0.13,0.30]	19.7	47	5.1	42	100%
1.3	7%	70%	98	22.5	0.233	0.162	0.173	[0.11,0.27]	18.9	47.8	4	43.2	100%

BAT IRM 14

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	36%	44%	31	24.2	0.5	0.7	0.749	[0.48,1.16]	28.1	38.8	22	24.5	23%
0.5	33%	47%	32	23.7	0.489	0.596	0.638	[0.41,0.99]	27.2	39.2	19.6	27.2	49%
0.6	29%	50%	36	23.7	0.464	0.509	0.545	[0.35,0.84]	26.3	40.1	17.3	29.5	76%
0.7	26%	53%	41	23.7	0.437	0.435	0.466	[0.30,0.72]	25.5	41	15.1	31.8	92%
0.8	23%	56%	47	23.6	0.412	0.373	0.399	[0.26,0.62]	24.6	41.9	13	33.9	98%
0.9	20%	59%	54	23.5	0.386	0.321	0.344	[0.22,0.53]	23.8	42.7	11	35.9	100%
1	17%	62%	63	23.3	0.361	0.278	0.298	[0.19,0.46]	23	43.6	9.2	37.7	100%
1.1	14%	64%	75	23.1	0.338	0.243	0.26	[0.17,0.40]	22.2	44.4	7.6	39.4	100%
1.2	12%	66%	88	22.9	0.316	0.215	0.23	[0.15,0.36]	21.5	45.2	6.3	40.9	100%
1.3	9%	68%	103	22.7	0.296	0.192	0.205	[0.13,0.32]	20.8	45.9	5.1	42.1	100%

BAT IRM 15

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	37%	43%	30	24.2	0.534	0.746	0.798	[0.51,1.24]	28.7	38.2	22.7	23.9	16%
0.5	34%	45%	31	23.7	0.533	0.647	0.692	[0.45,1.07]	28	38.4	20.4	26.4	35%
0.6	31%	49%	34	23.8	0.514	0.561	0.6	[0.39,0.93]	27.2	39.2	18.2	28.6	60%
0.7	28%	52%	38	23.7	0.493	0.486	0.52	[0.34,0.81]	26.5	39.9	16.1	30.7	82%
0.8	25%	55%	44	23.6	0.472	0.422	0.452	[0.29,0.70]	25.8	40.6	14.1	32.8	94%
0.9	22%	57%	50	23.5	0.45	0.368	0.394	[0.25,0.61]	25.1	41.4	12.2	34.7	98%
1	19%	60%	59	23.4	0.428	0.322	0.345	[0.22,0.53]	24.4	42.1	10.4	36.5	100%
1.1	16%	62%	71	23.2	0.407	0.284	0.304	[0.20,0.47]	23.8	42.8	8.8	38.2	100%
1.2	14%	64%	87	23	0.386	0.252	0.27	[0.17,0.42]	23.1	43.5	7.4	39.7	100%
1.3	11%	66%	106	22.8	0.367	0.226	0.242	[0.16,0.37]	22.5	44.1	6.2	41	100%

BAT IRM 16

k	BAT 3y	GPS 3y	GPS mOS	pool mOS	HR@IA	HR@80	HRobs	95% CI	BATa@IA	GPSa@IA	BATa@80	GPSa@80	P(win)
0.4	38%	42%	29	24.1	0.568	0.791	0.847	[0.55,1.31]	29.3	37.6	23.3	23.3	10%
0.5	35%	44%	30	23.7	0.577	0.697	0.746	[0.48,1.16]	28.7	37.7	21.2	25.7	24%
0.6	32%	47%	33	23.8	0.565	0.613	0.656	[0.42,1.02]	28.1	38.3	19.1	27.7	44%
0.7	29%	50%	36	23.7	0.551	0.539	0.577	[0.37,0.89]	27.5	38.9	17.1	29.7	67%
0.8	27%	53%	41	23.7	0.535	0.475	0.508	[0.33,0.79]	26.9	39.5	15.2	31.7	84%
0.9	24%	55%	47	23.6	0.518	0.419	0.448	[0.29,0.69]	26.4	40.1	13.3	33.5	94%
1	21%	58%	55	23.5	0.5	0.37	0.396	[0.26,0.61]	25.8	40.7	11.6	35.3	98%
1.1	18%	60%	67	23.3	0.482	0.329	0.352	[0.23,0.55]	25.3	41.3	10.1	36.9	100%
1.2	16%	62%	83	23.2	0.464	0.294	0.315	[0.20,0.49]	24.7	41.9	8.6	38.4	100%
1.3	14%	64%	108	23	0.446	0.265	0.283	[0.18,0.44]	24.2	42.4	7.3	39.8	100%

Welcome to the $SLS daily discussion hub! Whether you’ve got a gut feeling or just need to vent, this is the place to ask questions, share insights, and talk about daily price action.

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Alright fellow weaponized bagholders, help me out with my regarded math.

REGAL is event-driven. The trial ends at 80 deaths.

Originally, everyone expected we'd hit 80 events a long time ago.

Instead:

• ~60 events in early 2025
• ~72 events by the end of 2025
• and we're STILL waiting

So here's my smooth-brain question:

How much value do we theoretically gain every day that the trial keeps going?

If BAT was behaving as expected, wouldn't the events keep rolling in?

Instead, every day that passes without event #80 means that someone who was expected to die... didn't (at least not yet).

Now I know there are other explanations:

• better supportive care,
• venetoclax,
• better salvage therapy,
• enrollment effects,
• randomness,

...but the protocol's assumptions already accounted for expected survival when they powered the study.

So unless the statisticians from SELLAS, the investigators, and the FDA all suddenly forgot how AML works, doesn't every additional day shift the odds at least a little toward GPS working?

I'm not saying each day adds 0.1% probability of success or anything stupid like that.

I'm asking whether anyone here (or any biostatisticians lurking) has actually modeled this.

For example:

• If event #80 was expected in Month X but instead arrives 6 months later, how much would that typically change the implied hazard ratio?
• Can you estimate how much "hidden separation" accumulates with every delayed event?
• Or is it impossible to infer anything meaningful from blinded event timing alone?

Genuinely curious. If anyone has experience with event-driven oncology trials or survival analysis, I'd love to hear the math behind it.

(Also yes, I'm aware this is peak copium. I'm just trying to quantify the copium.)

After the recent move from approximately $8.50 to over $16, followed by the retracement into the $13 range and now relative recovery, I revisited the MRSPM framework to determine whether our original assumptions remained valid. Surprisingly, they do. What changed was not the model. What changed was the environment.

Reviewing the highest-velocity portion of the move revealed a previously unmodeled phenomenon that I now believe deserves its own designation: Meme Momentum Cascade Hypothesis (MMCH). Unlike traditional graphical reinforcement, MMCH occurs when multiple closely related graphical conviction artifacts are released in rapid succession. These are not independent observations but recursive derivatives of a common narrative construct—multiple caption variations, AI refinements, alternate edits, and callback expansions all released within a compressed time window.

Initially I viewed these as redundant. I now believe they function much more like synchronized wave interference. Individually, each artifact contributes only marginal signal density. Collectively, they generate what I can only describe as latent graphical momentum. This momentum is largely invisible while it is accumulating. However, once cumulative graphical density exceeds a critical threshold, recursive reinforcement becomes nonlinear and produces a Meme Momentum Cascade Event (MMCE), resulting in rapid community conviction expansion.

https://preview.redd.it/ooxitbvtq9bh1.png?width=1536&format=png&auto=webp&s=e84338c1b54a563c29143d8f4c48cd7d3a498497

Looking back, the timing aligns remarkably well with the recent appreciation from the mid-$8 range into the $16 area. Unfortunately, immediately following this period, an external variable entered the system. Graphical submissions began encountering increased moderation under Rule 8, reducing the observed rate of recursive graphical reinforcement. Whether intentional or simply an artifact of changing moderation practices is outside the scope of this analysis, but from a purely modeling perspective the effect is indistinguishable from an abrupt reduction in available signal input.

Within the MRSPM/MMCH framework, this does not destroy accumulated conviction mass. Instead, it interrupts the cascade before full saturation can occur. This would naturally be expected to produce exactly what we've observed: a flattening of momentum and partial retracement despite the underlying recursive signal reservoir remaining elevated. In other words, the model doesn't suggest conviction disappeared.

https://preview.redd.it/zxfj17ck1abh1.png?width=1536&format=png&auto=webp&s=108d59a4bc272aeb32dba89c03fcc9239062add6

It suggests the amplifier was unplugged. Let's plug it back in and crank to 11.

Future work will focus on quantifying what I'm tentatively calling the Graphical Suppression Coefficient (GSC)—a measure of how reductions in observable graphical reinforcement influence downstream Meme Momentum Cascade Events. My current hypothesis is that suppression delays saturation rather than preventing it, implying that accumulated latent signal may simply require a new catalyst before resuming recursive expansion.

Welcome to the $SLS daily discussion hub! Whether you’ve got a gut feeling or just need to vent, this is the place to ask questions, share insights, and talk about daily price action.

SLS is a small-cap biotech company that currently awaits binary results of its phase 3 Regal trial. Daily price action is volatile. Do not invest what you cannot afford to lose as successful trial results are not guaranteed.

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One thing I don't think enough people are talking about is the Expanded Access Program (EAP).

We all know the REGAL Phase 3 trial is blinded, so SELLAS can't see who's receiving GPS versus control or how the trial is ultimately performing until it's unblinded.

But the EAP is different. Patients receiving GPS through Expanded Access are outside the blinded REGAL trial. That means safety and patient outcomes from those EAP cases can still be observed and collected.

Obviously, EAP data doesn't prove Phase 3 success—there's no randomized control group—but it can give physicians and the company additional real-world experience with GPS while REGAL continues.

The fact that doctors are requesting access to GPS for eligible patients, combined with the encouraging earlier-phase data and the continued delay in reaching the 80th event, is what keeps me interested.

Question for everyone: If you were management and you were seeing encouraging real-world experiences through the EAP (without being able to see the blinded REGAL data), how much confidence would that give you heading into the final readout?

Welcome to the $SLS daily discussion hub! Whether you’ve got a gut feeling or just need to vent, this is the place to ask questions, share insights, and talk about daily price action.

SLS is a small-cap biotech company that currently awaits binary results of its phase 3 Regal trial. Daily price action is volatile. Do not invest what you cannot afford to lose as successful trial results are not guaranteed.

This thread auto-publishes every day at 12am EST.

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Welcome to the $SLS daily discussion hub! Whether you’ve got a gut feeling or just need to vent, this is the place to ask questions, share insights, and talk about daily price action.

SLS is a small-cap biotech company that currently awaits binary results of its phase 3 Regal trial. Daily price action is volatile. Do not invest what you cannot afford to lose as successful trial results are not guaranteed.

This thread auto-publishes every day at 12am EST.

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Like a lot of you, I’ve been staring at the REGAL event-milestone drip (60, 72, 78 deaths) trying to figure out what it implies. So I built a tool that turns the guesswork into something you can actually turn knobs on, and put it online. It’s free, self-contained, runs entirely in your browser, and touches zero private data.

Link: https://moalbaek.github.io/Regal_modeling/
Code + full methodology docs: https://github.com/moalbaek/regal_modeling

Not investment advice. It’s a research tool built 100% from public disclosures (press releases, 8-Ks, ClinicalTrials.gov, the trial-design paper). It does not and cannot unblind the trial.

The one fact that makes REGAL hard to read
REGAL is blinded. Every public number, 60 deaths by Dec 2024, 72 by Dec 2025, 78 by May 2026, is a pooled count across both arms. We never get the GPS-vs-BAT split.

That has a sharp consequence that the tool is built around:

• The pooled survival curve is recoverable from the death milestones plus enrollment timing.
• Splitting that pooled curve into the two arms is not. It requires you to assume how good the control (BAT) arm is.

So every “probability of success” you’ll ever see for REGAL, mine included, is really a function of one assumption: how good is best available therapy? The tool’s whole job is to make that assumption explicit and let you move it.

The anomaly driving everything: accrual has run at roughly 1 death per month. A CR2 AML cohort mostly 2 to 4 years out from randomization should be dying much faster than that. Something is keeping these patients alive far longer than the ~6 to 8 month historical control. The model exists to ask: what, and is it GPS?

What the tool does
1. Calibrates the pooled survival curve to the disclosed 60/72/78 milestones.
2. Lets you set the BAT arm (composition of observation / HMA / venetoclax / LDAC, the venetoclax “cure” fraction, how healthy the enrolled population is, natural death rate, dropout, etc.).
3. Monte-Carlo simulates the trial’s actual pre-specified test, a stratified Cox model, one-sided 0.025, success at HR ≤ 0.636 over 80 deaths, and reports P(success).

At the default “base” assumptions, the headline plateau P(success) lands around ~94%.

The enrollment criteria are a bigger deal than they look, and there’s a slider for them
Here’s a trap worth internalizing. The survival numbers you’d pull from the literature for each BAT therapy (venetoclax, HMA, LDAC, etc.) describe all transplant-ineligible CR2 patients on that drug. But a Phase 3 trial doesn’t enroll all-comers. REGAL had an eligibility bar (performance status, organ function, and notably an “estimated life expectancy > 6 months” criterion). That screens in a healthier subset than the real-world population those textbook medians came from.

Why that matters: a healthier enrolled control arm can outlive its own face-value inputs, which means part of REGAL’s slow, encouraging death accrual could be selection, not GPS.

The tool makes this an explicit lever, an enrollment-selection slider (0 to 50%, default 25%) that models the eligibility filter as keeping the strongest fraction of patients (a left-truncation, with a direct correlate in that “>6 months life expectancy” criterion). Play with it and you see how much it moves things:

• Slide selection 0, 25, 50% and the modeled BAT arm lifts from ~9 to ~14 to ~22-month median OS, with its durable-survivor fraction climbing ~14% to ~19% to ~29%.
• To keep the pooled 60/72/78 milestones pinned, the fit then attributes less to GPS, and the headline plateau P(success) drops from ~100% to ~94% to ~13%.

There’s also a nice internal tell: at zero selection the raw component medians actually over-produce early deaths versus the real milestones (the model wants ~65 deaths where reality shows 60), i.e. the data are telling you some enrollment enrichment is needed to explain how slowly people are dying. Push it too far the other way and the control arm becomes implausibly healthy. Somewhere in that band is your real view. Alongside venetoclax quality, this is the second big “how good is the control arm” knob, and the one most people forget exists.

What else you learn by playing with it
The answer is almost entirely the BAT assumption. Between the composition/venetoclax knobs and the enrollment slider, P(success) swings from ~100% down to single digits. There’s really only one bear case that pushes it clearly below 50%, the “bear corner,” where you assume venetoclax maintenance is both dominant in the control arm and ~30 to 36% curative. Whether you believe that is the whole ballgame.

Fancier structure barely moves the needle. I added component-mixture BAT and a GPS immunological non-responder subgroup (anchored to the ~80% WT1 T-cell response rate). Both get absorbed by the pooled fit and leave P(success) roughly unchanged, because the blinded data only pin the pooled curve, so any extra structure just gets redistributed. Useful lesson: added detail does not equal added information when the data are blinded.

The honest hard question isn’t “is the plateau real,” it’s “is the plateau GPS-specific?” There’s a second panel that holds the BAT arm bit-for-bit identical and swaps only GPS from a “cure” to a no-cure heavy-tailed survivor curve, then asks: can the milestones be reproduced without a GPS-specific benefit? At base assumptions the verdict is “not excluded.” A no-cure GPS tail sitting on top of venetoclax’s own plateau also fits the data. In plain English: the blinded milestones alone cannot prove the good pooled survival is coming from GPS rather than from a strong, well-selected control arm. You only force a “GPS cure is required” verdict when you credit BAT quite little (low venetoclax, low selection).

That’s the real takeaway. The milestones are genuinely encouraging (survival is running well above historical control), but by construction they can’t tell you which arm the benefit lives in. What they can do is bound the bear case: to get a bad readout you have to assume a specific, fairly aggressive story about how good, and how healthy-at-enrollment, modern venetoclax-based BAT is.

Go break it. The most useful thing you can do is find the BAT assumptions you believe, including how much you think the eligibility criteria enriched the cohort, and see what P(success) falls out, and whether the “GPS-specific” verdict flips. If you can articulate why your BAT and enrollment settings are right, you’ve articulated your actual thesis on the stock. Feedback and “your assumption X is wrong because Y” very welcome. The per-component survival numbers and the selection band are the intended things to argue about.

Interesting timing...

GenFleet just announced a HK$350 million share buyback, stating it believes its shares are undervalued:
https://www.minichart.com.sg/2026/06/29/genfleet-therapeutics-announces-hk350-million-h-share-repurchase-to-enhance-shareholder-value-2026/

Why does that matter to SELLAS investors?

GenFleet is the company that discovered and is developing SLS009 (GFH009), which it licensed to SELLAS.

Also, according to ClinicalTrials.gov, the Phase Ib/II PTCL study of SLS009 reached its listed study completion date on June 30, 2026:
https://clinicaltrials.gov/study/NCT05934513

A few reminders:

• Early PTCL data showed 4 responses in 11 evaluable patients (36.4% ORR).
• PTCL is a rare, aggressive T-cell lymphoma with limited treatment options.
• Positive updated PTCL data would provide additional validation of SLS009 beyond AML.

REGAL remains the primary near-term catalyst for SELLAS, but encouraging PTCL data would strengthen the broader SLS009 story and could expand its commercial potential.

Interesting few weeks ahead.

First off, thanks to a lot of people here who have helped me understand the REGAL trial better. I'm not a statistician (still a student) and this is definitely not meant to replace the more sophisticated models others have shown. Still, I felt like this would be useful to show... So buckle up!

Basic Assumptions

1. Total REGAL patients: 126
2. Arm split: 63 BAT / 63 GPS
3. Median enrollment date: March 22, 2023 (this is used by u/Confident-Web-7118)
4. Known event anchors used: 60 events at December 10, 2024; 72 events at December 26, 2025; 78 events at May 11, 2026
5. Weibull shape parameter (k = 0.85)
6. -> This is meant to approximate a front-loaded AML-like survival curve rather than a simple exponential curve
7. Bat mOS tested: 6 months to 20 months
8. GPS mOS: solved by fitting the model to the 60/72/78 event anchors
9. HR approximation: assumes BAT and GPS use the same (k = 0.85) weibull shape

Again, this is not a true patient-level Cox model. It compresses the trial into a median-enrollment framework. But it helps to answer the following simple question: Given the public event timeline, what GPS mOS / HR would be implied under different BAT mOS assumptions?

The survival function used
The model uses this survival function: S(t)=0.5ˆ{(t/mOS)ˆk}

Where:

• (S(t)) = survival probability at time (t)
• (t) = months of follow-up from median enrollment
• (mOS) = median overall survival
• (k = 0.85)

Deaths in each arm are then calculated as: Death = 63 * (1 - S(t)), since there are 63 patients in each arm.

How the Python code works
For each Bat mOS from 6 to 20, the code does the following.

Step 1: Convert event dates into follow-up time

Using median enrollment of March 22, 2023:

• Dec. 10, 2024 = about 20.67 months
• Dec. 26, 2025 = about 33.18 months
• May 11, 2026 = about 37.65 months

Step 2: Pick a Bat mOS and calculate BAT deaths

For example, suppose:

Bat mOS = 10,

then the BAT survival curve is S_BAT (t) = 0.5 ^ {(t/10)ˆ0.85}.

At each event anchor, the model gets approximately:

Date	Total trial events	BAT alive	BAT deaths
December 10, 2024	60	17.44	45.56
December 26, 2025	72	9.22	53.78
May 11, 2026	78	7.42	55.58

Step 3: Subtract BAT deaths from total trial deaths

For example, if the trial had 60 total deaths, and BAT explains 45.56 of them, then GPS must explain the rest.

So, GPS_deaths = Total_deaths - BAT_deaths

For BAT mOS = 10:

Date	Total trial events	BAT deaths	Implied GPS deaths
December 10, 2024	60	45.56	14.44
December 26, 2025	72	53.78	18.22
May 11, 2026	78	55.58	22.42

So if BAT mOS = 10 months, then the public event path implies GPS death targets of roughly:
[14.44, 18.22, 22.42] across the three event anchors.

Step 4: Find the GPS mOS that best matches those GPS deaths

The code then tries many possible GPS mOS values. For each candidate GPS mOS, it calculates the predicted GPS deaths at the three dates (like how we calculated BAT deaths in Step 2). Then, it compares predicted GPS deaths versus the target GPS deaths.

The error is: Error = Predicted_GPS_Deaths - Target_GPS_Deaths.
The code then squares the error across the three anchors and adds them. Then, the code chooses the GPS mOS with the lowest total squared error.

For BAT mOS = 10, the best-fit result was GPS_mOS approximately 68.63

With this GPS mOS, the predicted GPS deaths are close to the target death path across all three anchors. The total squared error is approximately 3.227. The square root of that is approximately 1.8, so in simple terms, the fitted GPS curve is off by around 1.8 deaths across the 3 events.

Step 5: Convert Bat mOS and GPS mOS into rough HR.

Because both arms use the same Weibull shape (k = 0.85), the rough HR estimate is
HR = (Bat mOS / GPS mOS)ˆk

For BAT mOS = 10, the rough HR is (10 / 68.63)ˆ0.85 = approximately 0.195

So under this specific scenario:

• BAT mOS = 10 months
• Best-fit GPS mOS = 68.63 months
• Full-path rough HR = 0.195

*****************************************************************************************

The above was to show how the code works. The results of the model are shown below. In a similar fashion, it also calculates the HR at interim (60-events).

BAT mOS	Best GPS mOS	Squared Error	Full HR	60-event GPS mOS	60-event HR
6	120.32	20.9366	0.078	215.91	0.048
7	102.86	11.3595	0.102	139.27	0.079
8	88.83	6.0274	0.129	101.53	0.115
9	77.62	3.6581	0.160	79.58	0.157
10	68.63	3.2270	0.195	65.43	0.203
11	61.35	3.9860	0.232	55.63	0.252
12	55.38	5.4151	0.273	48.49	0.305
13	50.43	7.1648	0.316	43.07	0.361
14	46.27	9.0073	0.362	38.83	0.420
15	42.75	10.7991	0.411	35.44	0.482
16	39.73	12.4542	0.462	32.65	0.545
17	37.12	13.9254	0.515	30.34	0.611
18	34.84	15.1908	0.570	28.38	0.679
19	32.84	16.2453	0.628	26.70	0.749
20	31.07	17.0938	0.688	25.25	0.820

What stands out to me

The lowest squared-error happens around BAT = 9,10,11, which leads me to think that these are the most probable BAT mOS values for REGAL. These BAT mOS gives HR values of
0.160, 0.195, and 0.232.

What about halting for efficacy at interim?

One fair question is that at BAT mOS of 9, 10, 11 or even values higher, the HR is spectacular, why didn't they halt for efficacy?

I believe there can be a few possible reasons for this.

1. The company amended the final event count from 105 events to 80 events, which already shortened the trial and reduced the event burden.
2. A trial can look very favorable at interim but still continue if the sponsor, IDMC, and statistical plan favor more mature final analysis.
3. In immunotherapy-like settings, long-term follow-up may matter more than simply stopping as soon as a favorable signal appears.

Others who are more familiar with this side can answer though!

However, there are lots of caveats in the analysis!!

Limitations:

• It uses median enrollment rather than actual patient-level enrollment dates (Stergiou has said Sellas knows the exact enrollment dates and speaks very bullishy, so I don't view this as a negative factor).
• It assumes the same Weibull shape (k = 0.85) for both BAT and GPS.
• It does not model censoring properly.
• It does not perform a real Cox proportional hazards analysis.

All the above was calculated through Python code after I guided ChatGPT step by step through the logic. I don't know how to attach the code file here? If anyone is interest, DM me and we can figure it out!

Welcome to the $SLS daily discussion hub! Whether you’ve got a gut feeling or just need to vent, this is the place to ask questions, share insights, and talk about daily price action.

SLS is a small-cap biotech company that currently awaits binary results of its phase 3 Regal trial. Daily price action is volatile. Do not invest what you cannot afford to lose as successful trial results are not guaranteed.

This thread auto-publishes every day at 12am EST.

❗ Minimum requirements to post: account age of 28d and karma of 300. Your comment will be automatically deleted by the automod if you do not meet this threshold.