2007 -Beyond the HIV-Causes-AIDS Model

October 30, 2007

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“More than 20 years into the AIDS era, it has become increasingly clear

that the current single-virus causation model is lacking in predictive and

explanatory power.” This is how Culshaw, assistant professor of

mathematics at the University of Texas, Tyler, USA, begins her most recent

paper published in the winter 2006 issue of the Journal of American

Physicians and Surgeons [1].

Culshaw has announced why she “quit HIV” in March 2006 [2] (On Quitting

HIV, this series), and this had lured me onto the fascinating trail of how

a bad mathematical model has misguided AIDS policies for so many years

worldwide, and more importantly, alerted me to recent attempts to find

alternative, more realistic models.

ISIS Press Release 03/04/07

Beyond the HIV-Causes-AIDS Model

Dr. Mae-Wan Ho follows the trail of how a bad mathematical model has misled

AIDS policies with disastrous consequences, and recent attempts to find a

better model

A fully referenced version of this article is posted on ISIS members’

website. Details here

An electronic version of this report, or any other ISIS report, with full

references, can be sent to you via e-mail for a donation of £3.50. Please

e-mail the title of the report to: report@...

Alternatively, order you copy of ISIS Report Unraveling AIDS and get a free

CD of all new articles and others on AIDS from SiS archives to bring you

right up to date http://www.i-sis.org.uk/onlinestore/books.php#236

“A model lacking in predictive and explanatory power”

“More than 20 years into the AIDS era, it has become increasingly clear

that the current single-virus causation model is lacking in predictive and

explanatory power.” This is how Culshaw, assistant professor of

mathematics at the University of Texas, Tyler, USA, begins her most recent

paper published in the winter 2006 issue of the Journal of American

Physicians and Surgeons [1].

Culshaw has announced why she “quit HIV” in March 2006 [2] (On Quitting

HIV, this series), and this had lured me onto the fascinating trail of how

a bad mathematical model has misguided AIDS policies for so many years

worldwide, and more importantly, alerted me to recent attempts to find

alternative, more realistic models.

Perhaps quitting HIV is not the same as quitting AIDS, as the world is

desperately in need of a good model, to save lives and end human suffering

on a gigantic scale.

AIDS disease is generally characterised by a decline in CD4+ T lymphocytes

circulating in the blood, which are responsible for cell-mediated immunity.

As a result, the patient becomes susceptible to opportunistic infections

(those affecting weakened immune systems) such as tuberculosis, pneumonia,

meningitis, and other diseases caused by parasites, bacteria and viruses

that can enter and multiply in the cells of the body.

But models that assume the human immunodeficiency virus (HIV) plays a

central role in disease progression run into considerable difficulties. If

the decline in CD4+ cells is due to HIV killing the cells, then there

should be a correlation between the ‘viral load’, which estimates the

amount of virus in the body, and the CD4+ cell count. But that is not the

case. CD4+ cell count is not a reliable indicator of disease progression at

all, nor for that matter is viral load [3] (Chapter 2, Unraveling AIDS,

ISIS Report), and they bear little relationship to each another. This has

been confirmed in a recent study on untreated HIV+ individuals [4].

Although higher viral loads are associated with greater CD4+ cell decline,

only a very small proportion of CD4+ cell loss, about 4 – 6 percent, is

influenced by viral load. The authors reporting the new study called for

future efforts [4] “to delineate the relative contribution of other

mechanisms.” In short, as Culshaw states [1]: “It has been extremely

difficult to construct a realistic theoretical model of immune suppression

that is entirely mediated by HIV.”

Why is it important to have a realistic model of the disease? A realistic

model not only can predict how the disease will progress, it can also help

in developing effective treatment and prevention. Since the discovery of

HIV, mathematical models have been constructed precisely for those

purposes: to determine the rates of progression to AIDS, to define optimal

drug regimens for therapy, to develop vaccines, and as a desperate last

resort, microbicide vaginal gels [5] (Concentrating Exclusively on Sexual

Transmission of HIV is Misplaced, this series). However, the vast majority

of the models lack predictive power because the mechanisms of disease and

the fundamental nature of the immune system are both poorly understood.

Meanwhile, the consequences of models based on a wrong hypothesis are all

too clear, as Culshaw has starkly stated [2].

The toxicity of HAART treatment is now widely accepted [3] (Unraveling

AIDS, Chapter 7). The scandal of toxic drugs being tested on defenceless

foster children in New York City and mothers and babies in Uganda [6-8] (US

Children Used in AIDS Drugs Tests; Guinea Pig Kids in AIDS Drugs

Trials ; NIH-Sponsored AIDS Drugs Tests on Mothers and Babies ; SiS 27) was

widely publicised more recently in Harper’s Magazine [9]. This has reopened

the acrimonious debate [10] between AIDS ‘dissidents’ and the orthodox

community of researchers and activists led by Gallo, the

controversial co-discoverer of HIV. The litany of vaccine failures has

reached epic, controversial proportions [3] (Unraveling AIDS, Chapters

9-13); and the third large-scale clinical trials of anti-HIV gels has just

been terminated because it was not only ineffective, but actually increased

the risk of HIV infection [5]. There are many compelling reasons to

confront the bad model itself.

The Ho/Shaw model on why “hit hard hit early”

The model of HIV causes AIDS disease that has come to dominate global

policies on AIDS, from diagnosis to therapy and prevention, is barely 12

years old. It was created in two high profile papers published in the 12

January 1995 issue of the journal Nature [11, 12]. Two research teams, led

respectively by Ho of the Diamond AIDS Research Centre NYU

School of Medicine, New York, and Shaw of the University of Alabama

at Birmingham, used experimental antiretroviral drugs to follow how HIV

viral load and CD4+ cell counts change after drug administration. From the

changes, they estimated the rates of viral replication and elimination from

the body as well as the rates at which CD4+ cells are killed and replaced

by cell proliferation.

The results were astonishing; they were touted as giving a radically new

understanding of HIV infection, one in which the immune system is in a

constant battle with HIV from the moment of initial infection. As the

distinguished late mathematician Serge Lang, a prominent AIDS dissident

wrote [13]: “These papers largely provided the justification for the new

phase of protease inhibitor and cocktail treatments, as well as for the

expanded use of surrogate markers such as “viral load” and CD4 counts for

AIDS disease. Each of these represented a significant departure in terms of

HIV/AIDS diagnosis, maintenance, treatment, and epidemiological reporting.”

In the Ho study [11], a protease inhibitor code named ABT-538 was given at

600 to 1 200 mg per day to 20 HIV+ individuals whose pre-treatment CD4+

lymphocyte counts ranged from 36 to 490 per mm3 and viral load, measured by

a new quantitative branch polymerase chain reaction from 15 – 554 x 103

virus particles per ml.

Following treatment, every patient had a rapid and dramatic decline in

plasma viral load over the first two weeks, between 11 and 275-fold

reduction, with a mean of 66-fold, i.e., a 98.5 percent drop. The initial

decline was assumed to be exponential, allowing the half time of viral

decay (time it takes for half of the virus particles to disappear) to be

estimated as 2.1 + 0.4 days. That showed HIV-replication must be “highly

productive”, the authors claimed; and the virus particles were cleared as

fast as they were produced. In other words, a steady state standoff was

established in the body, so that the viral load measured at any time

remained roughly the same. The estimated minimum production rate – the same

as the minimum clearance rate - averaged 0.68 + 0.13 x 109 virus particles

per day, which is really quite modest, considering that each infected cell

can produce a hundred virus particles.

The paper was heavily criticised. The estimates depended on the assumption

that drug treatment does not affect viral clearance, and that there was a

pre-existing steady state between viral production and viral clearance,

regardless of the amount of virus in circulation. Curiously, the estimated

viral clearance/production rate bore no relationship to the initial viral

load or to the CD4+ lymphocyte count, which was difficult to reconcile with

the idea that the virus was killing the CD4+ cells by invading the cells to

replicate and burst the cells. In that case, the more virus particles and

the more cells, the higher should be the production/clearance rate.

After ABT-538 treatment, CD4+ lymphocyte counts rose in each of 18 patients

that could be evaluated. Some increases were dramatic and others quite

modest. From the slope of the line depicting the rise in CD4+ lymphocyte

counts assuming an exponential increase, a doubling time of about 15 days

was estimated during the (assumed) pre-treatment steady state. The slopes

were inversely correlated with baseline CD4+ cell counts, however, which

too was difficult to explain. In patients with lower initial CD4 cell

counts, more prominent rises were obtained. Nevertheless, the authors

claimed: “This demonstrates convincingly that the CD4+ lymphocyte depletion

seen in AIDS is primarily a consequence of the destruction of these cells

induced by HIV-1, not a lack of their production.” They explained that

such an inverse correlation would be expected if T-cell proliferation were

governed by some kind of homeostatic mechanism. From the inverse

correlation, it was estimated that the minimum number of CD4+ cells in

blood produced or destroyed each day ranged from 4.3 x 106 to 109 x 106,

with a mean of 35.1 x 106. As the blood lymphocyte pool is about 2 percent

of the total population, the overall CD4+ lymphocytes turnover in the

patients was calculated to vary from 0.2 x 109 to 5.4 x 109 cells per day,

with a mean of 1.8 x109 cells per day. This number of cells was about the

same as the number of putative viruses produced (and cleared) each day, far

too many cells killed for the number of viruses produced. Things didn’t add

up.

The increase in CD4+ lymphocyte counts following ABT-538 administration was

also modelled linearly, and using the same arguments as for the decline in

viral load, the minimum estimates of total CD4+ lymphocytes production or

destruction rates at baseline were determined to vary between 0.1 x 109 to

7.8 x 109 cells per day with a mean of 2.6 x 109 cells per day,

sufficiently close to the estimate above.

The authors commented that the CD4+ lymphocyte depletion seen in advanced

HIV-1 infection “may be likened to a sink containing a low water level,

with the tap and drain both equally wide open.” As the regenerative

capacity of the immune system is not infinite, it is not difficult to see

why the sink eventually empties (when CD4+ cells are all depleted).

Now comes the crucial conclusion that has justified the “hit hard, hit

early” [14] strategy of HAART that has gone so disastrously wrong for

otherwise healthy HIV+ individuals: “It is also evident from this analogy

that our primary strategy to reverse the immunodeficiency ought to be to

target virally mediated destruction (plug the drain) rather than to

emphasize lymphocyte reconstitution (put in a second tap).”

And: “We believe our new kinetic data have important implications for HIV-1

therapy and pathogenesis. It is self evident that, with rapid turnover of

HIV-1, generation of viral diversity and the attendant increased

opportunities for viral escape from therapeutic agents are unavoidable

sequelae. Treatment strategies, if they are to have a dramatic clinical

impact, must therefore be initiated as early in the infection course as

possible, perhaps seen during seroconversion…”

The Shaw study [12] used the protease inhibitors ABT-538, L-735.524, or the

non-nucleoside reverse transcriptase inhibitor Nevirepine on a total of 22

patients, as part of a phase I/IIA clinical trial, and came to the same

conclusions. In addition, it found drug resistant mutant viruses in all

subjects soon after treatment started. The lowest point of viral load was

at two weeks in all subjects after treatment started, when the CD4+ cell

count rose to a peak. Thereafter, viral load increased rapidly, despite

increased drug dosage, and by week four, 100 percent of the virus in blood

was drug resistant. The CD4+ cell counts dropped more slowly, and were back

to baseline within 6-20 weeks.

Critics faulted the Shaw study for the same unwarranted assumptions that

underlie the Ho study. Neither study included a control group. The clinical

outcomes of the drugs on the patients were not reported, so it was

impossible to tell whether the patients benefited from the transient

reduction in viral load or the transient increase in CD4+ cells. The

mathematical model had no contact with the observations other than dubious

fitting of a straight line through two or three data points [15].

The Ho/Shaw model began to unravel almost as soon as it was proposed, but

the “hit hard hit early” HAART approach continued at least until 2001 when

the US government’s expert panel on anti-HIV therapy finally recommended

restricting the prescription of anti-HIV drugs for as long as possible for

people without symptoms, on account of the serious side effects [3]

(Unraveling AIDS, Chapter 7).

“The final nails in the coffin” of Ho/Shaw models

Rodoerer at Stanford University Beckman Center, writing in News and

Views of the February 1998 issue of Nature Medicine commented [15] that two

papers published in the same issue [16, 17] “provide the final nails in

the coffin for models of T cell dynamics in which a major reason for

changes in T cell numbers is the death of HIV-infected cells [i.e., the

Ho/Shaw models].”

The papers presented extensive data on the remodelling of the T cell

compartment in HIV-infected individuals after treatment with HAART.

Throughout the early stages of HIV infection, CD4+ cells decline, whereas

the total CD8+ cells expand. However, the application of flow cytometry

techniques that accurately identified subsets of T cells showed that this

increase in CD8+ cells is made up entirely of memory and activated T cells,

while naïve T cells (precursor of memory and activated T cells) declined at

the same rate as naïve CD4+ cells (precursor of memory and activated CD4+

cells). Activated T cells are found only in peripheral tissues - the spleen

and lymph nodes – and their expansion in the blood in HIV-infected

individuals indicated an active immune response even during the later

stages of disease.

Within weeks after starting HAART, there were significant increases in the

number of B cells, and of CD4+ and CD8+ cell in the blood, but these were

only memory cells that can maintain long-term residence in lymph nodes, and

not naïve T cells, which do not dwell in lymph nodes and do not immediately

respond to HAART.

Essentially, the studies provided evidence for the ‘redistribution

hypothesis’: the increase in CD4+ cell counts observed shortly after the

start of HAART are T lymphocytes redistributed from the lymph nodes, and

not produced by cell proliferation. During active viral replication and the

concomitant cellular immune response, a large number of B and T cells may

be trapped in peripheral sites (for example, by antigen, cytokine or

chemokine signals). After initiation of HAART, when HIV is effectively

removed from the system, the immune response begins to resolve and cells

pour out of the inflamed lymph nodes back into the blood.

The first study [17] suggested that the degree of T lymphocyte trapping

increases as disease progresses. That would explain why the response to

HAART tends to be greater in individuals with lower CD4+ cell counts.

Functional recovery of the T cell compartment is only complete when the

repertoire of T cell receptors is restored, so that potentially all

antigens can be recognized. The decrease in naïve and memory T cell

populations during disease progression means that the repertoire becomes

increasingly restricted, finally resulting in immunodeficiency.

The second study [18] confirmed earlier findings that the T cell receptor

repertoire in HIV-infected individuals is significantly different from the

normal distribution found in healthy adults. This is due to a loss of

unique T cell clones and an expansion of antigen-specific clones caused by

an over-representation of certain receptor types.

In individuals responding to HAART, the number of naïve T cells slowly

increases over a six-month period after initiation of HAART and a

reconstitution of the T cell repertoire can take place (but see later).

Notably, this reconstitution occurs only in individuals who show reductions

in viral load in response to HAART. It is also likely that failure of

HAART, which occurs in many patients over time, will also be accompanied by

a re-initiation of cell losses and repertoire restriction.

AIDS and an over-stimulated and unbalanced immune system

The use of radioisotope labelling has enabled researchers to identify

different populations of T lymphocytes in the human body [19]. There are

long-lived and short-lived cells, and the size of the total T lymphocyte

pool appears to be regulated mainly at the level of the long-lived cells.

During the course of an antigen-driven cell proliferation response, some T

cells differentiate into effector cells that clear the antigens from the

body, and typically have a short life span. Others become memory T cells,

which, by contrast, are long-lived and serve as reservoirs for subsequent

activation by antigen to proliferate and produce effector cells. Naïve T

cells also have a long life span. In advanced HIV-1 infection, a much

higher proportion of T cells are short-lived, compared to healthy controls,

and effective HAART tends to restore the values towards the normal.

Advanced HIV-1 infection greatly reduces the percentage and total number of

CD4+ cells that are long-lived. Because these cells represent the

regenerative source of newly formed CD4+ effector T cells, their loss may

underlie the immunodeficiency of HIV-1 disease. These abnormalities may not

be present in early HIV-1 infection and may represent a marker of disease

stage.

However, many questions remain unanswered [20]. Why is HIV so uniquely

powerful, among chronic viruses, in inducing a chronic state of immune

activation? And why is the HIV- induced immune activation is so disruptive

of the proper overall functioning of the immune system?

Of course, there remains the lingering doubt that HIV is not actually

causing the disease

A radical new model is needed

None of the models so far has taken into account the role of nutrition in

AIDS or AIDS-like diseases, and the ability of good nutrition to reverse or

delay disease progression [3] (Unraveling AIDS, Chapters 15-17). In

particular, AIDS is a disease in which the immune system is out of balance,

not only in being chronically activated, but also in the predominance of

the humoural (type 2) at the expense of cellular (type 1) immunity [3]

(Unraveling AIDS, Chapter 12).

All HIV models so far have considered the CD4+ cells as a single entity.

But it has been known for some that the pool of CD4+ cells (commonly known

as T-helper or Th cells) contained two different subsets: Th 1, responsible

for cell-mediated immunity and Th 2, responsible for extracellular or

humoural immunity. The majority of the CD4+ Th 1 cells reside in the

peripheral blood and it is their depletion that occurs in the progression

to AIDS [1 and references therein]. Th2 cells reside mainly in the bone

marrow and to a lesser extent in the lymph nodes, and do not appear to

become depleted in the progression to AIDS. If anything they have been

observed to increase [21].

As AIDS progresses there appears to be a gradual shift from Th1- to

Th2-dominance, which is why patients experience mainly fungal and

mycobacterial infections, but very few “classical” bacterial diseases.

Furthermore, elevated levels of antibodies, including autoantibodies, are

characteristic of all AIDS patients, as consistent with an increase in Th2

subset. Contrary to what one might expect, HIV is expressed primarily in

Th0 (precursor of Th1 and Th2) and Th2 cells and is scarcely

Expressed in the Th1 subset [22]. Yet it is the Th1 cells that are

depleted, whereas the cells in which HIV prefers to reside do not decrease.

So what mediates the Th1 to Th2 shift, and how can it be prevented or

reversed so as to restore balance to the immune system?

Culshaw [1] suggests using bifurcation theory, a branch of mathematics that

deals with changes in critical parameters that determines major or abrupt

changes, such as the commitment of Th0 to become either Th1, or Th2.

One crucial component in the Th1 to Th2 shift is the release of nitrous

oxide (NO) from the cell-mediated arm of the immune system [23]. NO can

diffuse though cell membranes without the help of receptors in cell-cell

communication, and nitrogen oxides are regulated by the oxidative state of

the immune cells. Excessive oxidation negatively affects immune function

through the production of cytokines from the immune cells. Oxidative

processes are counterbalanced by reduction, which is accomplished by

sulphur-containing molecules that serve as electron donors, the main one is

glutathione, a tripeptide consisting of cysteine, glutamine and glycine.

Glutathione is found in both the reduced (GSH) and the oxidized (GSSG)

form. The ratio of GSH:GSSG has been shown to be important in regulating

Th1/Th2 balance [24, 25]. If the GSH:GSSH ratio declines, Th2 cells are

preferentially made from Th0, thus resulting in Th2 dominating at the

expense of Th1 cells.

HAART causes a transient increase in T-cell counts in the peripheral blood

because it damages B-cells as they mature and disrupts antibody production.

Unable to make contact with antibody-producing B-cells in the bone marrow,

the CD4+ Th2 cells return to the peripheral blood. So, although CD4+ T cell

count increases, the cells are ineffective against opportunistic

infections. This gives rise to the phenomenon of ‘immune reconstitution

syndrome’, in which patients experience the “irony” of an increase in

opportunistic infections after initiating HAART therapy [26].

Culshaw sketches out an alternative mathematical model based on the

GSH:GSSG ratio and Th1/Th2 balance, which are crucial in the development of

AIDS, and proposes that HIV itself need not even be included as a variable.

The proposed model tracks the Th0, Th1 and Th2 subsets of the T-cell pool

over time, with the ratio of GSH:GSSG as a possible bifurcation parameter.

As GSSG increases, and the ratio declines, a greater proportion of the Th0

cells mature into Th2 cells and are diverted from the Th1 pool. Such a

model could enable researchers to determine the critical ratio of GSH:GSSG

below which a shift to Th2-dominance occurs.

There are several advantages to such a model. First, it replaces viral load

measurements, which have not been shown to have good clinical predictive

value as a therapeutic endpoint. Second, determining a critical ratio of

GSH:GSSH rather than a critical value gets around the problem of

variability among individual patients. Finally, the model explicitly

considers the Th1/Th2 ratio, an important measure in the progression to

AIDS that has been largely neglected in theoretical modelling.

Circumstance evidence in favour of such a model is that selenium and other

antioxidants appear to be effective in preventing and treating AIDS [3]

(Unraveling AIDS, Chapter 17). Another advantage of Culshaw’s new model is

that it can make direct contact with nutritional status, an important

determinant in disease progression.

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